Yolov8 architecture paper pdf (2020, March). To address this issue, this paper presents an efficient optimized YOLOv8 model with extended vision (YOLO-EV), which optimizes the performance of the YOLOv8 model through a series of innovative 5 Architecture Components The YOLOv8 architecture is composed of two major parts, namely the backbone and head, both of which use a fully convolutional neural network. The plain single-path architecture is a better choice for small networks, but for larger models, the exponential growth of the parameters and the compu-tation cost of the single-path architecture makes it infeasi-ble; (2) Quantization of The cascade fusion algorithm YOLOv8-CB has higher detection accuracy and is a lighter model for multi-scale pedestrian detection in complex scenes such as streets or intersections, and presents a valuable approach for device-side pedestrian detection with limited computational resources. With the rapid development of autonomous driving technology, the demand for real-time and BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection MingKang,Chee-MingTing(B),FungFungTing,andRaphaëlC. Techniques such as multi-scale detection, context This paper presents a generalized model for real-time detection of flying objects that can be used for transfer learning and further research, as well as a refined model that achieves state-of-the-art results for flying object detection. YOLOv5 and YOLOv8 performed almost identically in recognizing classes such as hand, knife, and vegetable, whereas YOLOv5, YOLOv8, and YOLOv10 accurately identified the cutting board. 98%. Object detection models with slow inference times YOLOv8 delivers new features and capabilities by building on the breakthroughs of its predecessors, making it the best option for a wide range of object identification applications. The YOLOv8 network architecture consists of various. Dong, H. YOLOv8 architecture Figure 3. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces efficiency. This paper proposes the YOLOv8+GC model for fracture detection, which is an improved version of the YOLOv8 model with the GC block. v14i5. YOLOv8 is an object detection model that is based on the You Only Look Once (YOLO) family of algorithms. A comparative analysis with previous iterations, YOLOv5 and YOLOv7, is conducted, emphasizing the importance of computational efficiency in various applications. RESULTS AND DISCUSSIONS The dataset has a total of 21 different traffic sign categories in the split of 80/20 which includes 1376 training images and 229 testing images 63 TTIC, Vol. 35 MB, approximately 4/5 of YOLOv8. Advanced Backbone and Neck Architectures: YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved feature extraction and object detection performance. 2 PDF | p>This paper presents a novel approach for detecting faults in photovoltaic (PV) cells. We start by YOLOv8 is the latest iteration of this algorithm, which builds on the successes of its predecessors and introduces several new innovations. The YOLOv8-TDD adaptation incorporates Swin Transformers to leverage hierarchical feature processing with shifted windows, enhancing the model’s efficiency This paper offers a brief evaluation of the you only look once (YOLO) algorithm of rules and another prevalent variation. The results show that the train various versions of YOLOv8 for instance segmenta-tion on static images and assess their performance on the test dataset (videos). YOLOv8 does not yet have a published paper, so we lack direct insight into the direct research methodology and ablation studies done during its creation. In this research, we trained the YOLOv8 algorithm on our MJFR dataset sourced from Roboflow, specifically tailored to the task of binary face mask An improved YOLOv8 detection method is proposed for detecting distracted driving behavior and driver’s emotion. Therefore, you could use the architecture figure of YOLOv5 and mention the specific changes made in YOLOv8 in your paper. We present a comprehensive analysis of YOLO’s evolution, We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with This paper aims to provide a comprehensive review of the YOLO framework’s development, from the original YOLOv1 to the latest YOLOv8, elucidating the key innovations, differences, and In this paper, the YOLOv8 with its architecture and its advancements along with an analysis of its performance has been portrayed on various datasets in comparison with previous models of We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8. -W. This model is specifically designed to meet the rigorous demands of PPE detection, ensuring accurate results. (A) is the original image. Deep learning models like YOLOv8 have learning model based on the Yolov8 architecture. In the new network, a To address this issue, we propose ADA-YOLO, a light-weight yet effective method for medical object detection that integrates attention-based mechanisms with the YOLOv8 architecture. Journal of (DySnakeConv), and Biformer within the YOLOv8 architecture, aiming to address and overcome the limitations associated with traditional PCB inspection methods. (2024). This work aims to test the mask R-CNN architecture and the Download Free PDF. 7% on the HRIPCB and DeepPCB datasets, respectively, improving by 2. In contrast to the previous anchor-based method, YOLOv8 adopts an anchor-free approach, which locates objects based on their centers, and predicts the distances from them to the bounding box, thus removing the need for predefined · YOLOv8 Architecture. Yolov8 Model The latest version of YOLO is Yolov8, which was released in 2022 by Ultralytics [24]. [9] T. Full-size DOI: 10. The method employs a modified YOLOv5 architecture optimized for resource-efficient tomato detection. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving. Effective detection of road hazards plays a pivotal role in road infrastructure maintenance and ensuring road DOI: 10. This paper presents a comparative analysis of the widely accepted YOLOv5 and the latest version of YOLO which is YOLOv7. This improves the model’s Fig. Phan SchoolofInformationTechnology,MonashUniversity,MalaysiaCampus, PDF | YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56. Real-time video surveillance, especially CCTV systems, requires fast and accurate face detection. Backbone: This paper proposes a novel approach using the YOLOv8 model for real-time object detection in night-time conditions. YOLOv8 architecture [16]. Key innovations, including the CSPNet backbone for enhanced feature extraction, the FPN+PAN neck for superior multi-scale object detection, and the transition to This paper research focuses on the following objectives. View a PDF of the paper titled YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection, by Chun-Tse Chien and 4 other authors which incorporates the attention mechanism into the original YOLOv8 architecture. This decision was made because the architecture is suitable for software that needs to balance processing speed and accuracyon embedded or mobile platforms. Guo, J. Padding: “padding” refers to adding extra pixels around the edges of the input image (typically zeros) before applying convolution operations. 2)Since the performance of the YOLOv8-AM model based on GAM is unsatisfactory, In this paper, we presented a comprehensive analysis of YOLOv8, highlighting its architectural innovations, enhanced training methodologies, and significant performance improvements over previous versions like YOLOv5. Sample images after augmentation 6 Brahm Dave / Procedia Computer Science 00 (2019) 000–000 3. YOLOv8-based Waste Detection System for Recycling Plants: A Deep Learning Approach. This paper uses machine learning theory to design a variety of The memory size of the YOLOv8-HD model is 6. Our proposed method leverages the dynamic feature localisation and parallel regression for computer vision tasks through \textit{adaptive head} module. In order to accurately detect the usage of personal protective equipment (PPE) in real-time and alleviate manual inspection by supervisors or safety officers, the project implements the YOLOv8 architecture. RELATED WORK PDF | On Aug 30, 2023, Felix Gunawan and others published ROI-YOLOv8-Based Far-Distance Face-Recognition | Find, read and cite all the research you need on ResearchGate Conference Paper PDF Available Traditional camera sensors rely on human eyes for observation. Section 4 details the proposed enhanced YOLOv8. Experiments were carried out by training a custom model with both YOLOv5 This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. This paper provides a comprehensive survey of recent developments in YOLOv8 and discusses its bottleneck architecture of YOLOv8 is identical to YOLOv5 but the first con vo- lution’s kernel size is changed from 1x1 to 3x3. We present a comprehensive We do not have a standalone figure of the model architecture specifically for YOLOv8. Two neural networks are implemented, namely the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN), along with a new labeling tool that This work explores the segmentation and detection of tomatoes in different maturity states for harvesting prediction by using the laboro tomato dataset to train a mask R-CNN and a YOLOv8 architecture. A custom dataset comprising various objects captured in low-light environments YOLOv8 YOLOv8 is the latest version of the object detection model architecture, succeeding YOLOv5. Finally, we summarize the essential lessons from YOLO's development and provide a perspective on its This research paper presents a structured approach to address the critical concerns associated with water quality assessment and underwater waste detection, employing advanced machine learning Therefore, this paper improves YOLOv8 and proposes a network model for UA V micro-target detection, and the improved network structure is shown in Figur e 3 . Experimental results demonstrate that the Neck part of the YOLOv8 model architecture to improve global feature extraction and capture comprehensive image information. Utilizing YOLOv8 for specic object sizes and resource-constrained applications may entail computational costs. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with Transformers. YOLOv8 is the latest iteration of this algorithm, which builds on the successes of its predecessors and introduces several new innovations. new Paper tables with annotated results for YOLOv8-ResCBAM: YOLOv8 Based on An Effective Attention Module for Pediatric Wrist Fracture Detection into the original YOLOv8 network architecture. Its architecture, incorporating advanced components and training techniques, has Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study April 2024 Cogent Engineering 11(1) This paper is the first to provide an in-depth review of the YOLO evolution from the original YOLO to the recent release (YOLO-v8) from the perspective of industrial manufacturing. The experimental results on the GRAZPEDWRI-DX dataset demonstrate that the mean Average Precision calculated at Intersection over Union threshold of 0. This helps to examine the behavior of the feature from the Mask YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Yolo Versions Architecture: Review. The YOLOv8 architecture can be shown in Fig. Subsequently, the review highlights key architectural innovations introduced in each variant, shedding light on the This paper proposed an ensemble model that uses the YOLOv8 approach for efficient and precise event detection. 1 Proposed Architecture based on MobileNet & on YOLOv8 In this paper, the foundation is based on the MobileNets neural network architecture [22]. 93%, and F1-score of 79. View PDF HTML (experimental) Abstract: Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. With the rapid advancement of artificial Increasing awareness of the importance of head personal protective equipment in industrial and construction environments has become a major concern in efforts to improve occupational safety. The paper delves into the architecture of YOLOv8 and explores image preprocessing techniques aimed at enhancing detection This study proposes an innovative solution utilizing the Yolov8 architecture for fruit detection that not only surpasses existing benchmarks but also establishes a robust foundation for transforming fruit detection practices in agriculture. It achieves high accuracy while remaining computationally lightweight. Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. 1 Follower To tackle multi-scale object detection, the paper proposes the SEF module, an enhancement based on SEConv. Not forcing the same c hannel Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors - WongKinYiu/yolov7 This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous iterations like YOLOv5. You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor YOLOv8 Architecture: A Deep Dive. Deepl----Follow. Initially, the input video is converted into frames and pre-processed. 3. 4. Section 3 provides an overview of the YOLOv8 network architecture. To establish a benchmark, the Head: YOLOv8 introduces a decoupled head architecture that separates the classification and detection processes. 999/fig-6 Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study View PDF (open in a new window) PDF (open in a new window & Kumar, M. This principle has been found within the DNA of all Equipment Detection using YOLOv8" seeks to develop such a system for seamless identification of worker adherence to PPE usage protocols. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural components. One pivotal update in YOLOv8 is the shift to anchor-free detection, veering away from the anchor-box techniques of its predecessors [9]. With that said, we analyzed the repository and information available about the model to start documenting what's new in YOLOv8. Key innovations, including the CSPNet backbone for enhanced feature extraction, the FPN+PAN neck for superior multi YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Specifically, as illustrated in Fig. YOLO v3-Tiny: Object detection and recognition using one stage improved model [Paper presentation]. The methodology involves the creation of a custom dataset and encompasses rigorous training, validation, and testing processes. 7% compared to YOLOv8 To tackle the intricate challenges associated with the low detection accuracy of images taken by unmanned aerial vehicles (UAVs), arising from the diverse sizes and types of objects coupled with The YOLOv8 series offers a diverse range of models, each specialized for specific tasks in computer vision. 1 and bro-ken down into several main components View a PDF of the paper titled YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision, by Muhammad Hussain View PDF HTML (experimental) Abstract: This paper presents a comprehensive review of the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. Architecture YOLOv8 model architecture is used to propose the wild animal object detection, which is part of the YOLO (You Only Look Once) series of object detection models. YOLOv8 architecture was trained on this dataset with the . We achieve this by training our first (generalized) model on a data set containing 40 different classes of flying objects, forcing the View a PDF of the paper titled YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes, by Om M. BGF-YOLO contains an attention mechanism to focus more on important features, and feature pyramid networks to enrich feature representation by merging high-level This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. This modification This paper proposes a system for real-time traffic monitoring based on cutting-edge deep learning techniques through the state-of-the-art YOLOv8 algorithm, benefiting from its functionalities to The YOLO architecture has evolved with YOLOv8, which provides better performance, enhanced accuracy, and faster inference, making it an attractive choice for implementing face mask detection . —In the agricultural sector, the precise detection of fruits plays a pivotal role in optimizing harvesting procedures, minimizing waste, and ensuring the C. YOLOv8’s integration of the CSPNet backbone and the enhanced FPN+PAN neck has markedly improved feature extraction and This paper presents a comparative analysis of two advanced deep learning models-YOLOv8 and YOLOv10-focusing on their efficacy in vehicle detection across multiple classes such as bicycles, buses This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 algorithm, called the smart fire detection system (SFDS), which leverages the strengths of deep In this paper, we develop a novel BGF-YOLO architecture by incorporating Bi-level routing attention, Generalized feature pyramid networks, and Fourth detecting head into YOLOv8. YOLOv8 features a new backbone network which is a modified version of the CSPDarknet53 architecture [26] which consists of 53 convolutional The research assesses the robustness and generalization capabilities of the models through mAP scores calculated across the diverse test scenarios, underlining the sig-nificance of YOLOv8 in road hazard detection and infrastructure maintenance. However, the architecture of YOLOv8 is based on YOLOv5, with various modifications in terms of model scaling and architecture tweaks. YOLOv8 has several architectural improvements over In recent years, computer vision has experienced a surge in applications across various domains, including product and quality inspection, automatic surveillance, and robotics. then, we discuss the major changes in network architecture and training tricks for each model. The results pro- Paper tables with annotated results for A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. The paper will also provide a concise explanation of convolution neural networks (CNNs) and the EfficientNet architecture to recognize fruit using the Fruit 360 dataset. The objects in the pre-processed frames are detected using the YOLOv8. October 2023; YOLOv8 architecture f or object detection . YOLOv8's backbone network serves as its framework and is in charge of extracting features from the input picture. From its first version through YOLOv8, the paper discusses the YOLO architecture's core features and enhancements. Second, an ingenious Efficient Multi-Scale Attention (EMA) mechanism is integrated into The paper compares the effectiveness of the two dif- ferent detector types and suggests a way for dynamically choos- Third, YOLOv8 uses cutting-edge architectural elements like feature pyramid networks (FPN) to effectively capture multi- scale and contextual information. Through tailored preprocessing and architectural adjustments, we This indicates a notable 9. 11591/ijece. This paper presents YOLOv8, a novel object detection algorithm that builds upon the advancements of previous iterations, aiming to further enhance performance and robustness. Furthermore, hyperparameter tuning experi- Watch: Ultralytics YOLOv8 Model Overview Key Features. Model architecture . In the conventional YOLOv8 architecture, a decoupled head with two branches is used to Download PDF. YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. A unique Cross-Stage Partial (CSP) connection is introduced in the CSPDarknet53 design, which improves gradient flow PDF | Potholes are considered a vital danger to road safety. Existing methods ignore a fact that when input data undergoes A novel BGF-YOLO architecture is developed by incorporating Bi-level routing attention, Generalized feature pyramid networks, and Fourth detecting head into YOLOv8, and achieves state-of-the-art on the brain tumor detection dataset Br35H. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations(2020) at a Tibetan highland station. YOLOv8 structure: Four major parts make up the YOLOv8 network architecture: the input, the feature enhancement (Neck) [20], the backbone network, and the decoupling head (Head) [21]. 21% macro average on the test dataset. YOLOv8 architecture. A convolutional layer can The paper delves into the architecture of YOLOv8 and explores image preprocessing techniques aimed at enhancing detection accuracy across diverse conditions, including variations in lighting, road types, hazard sizes, and types. Request PDF | PCB defect detection based on YOLOV8 architecture | The paper discusses the key factors and trends in the design and production of printed circuit boards (PCB), which determine the This paper implements a systematic methodological approach to review the evolution of YOLO variants. pdf. PDF Abstract A lightweight visual transformer, MobileViTSF, is proposed and combined with the YOLOv8 model to address the loss of semantic information that arises from inconsistent scales in the detection of small ships and can achieve significantly improved accuracy for ship detection with fewer model parameters and a reduced model size. We start by describing the When both architecture performances are applied, YOLOv8 outperforms YOLOv5. Specifically, we respectively employ four attention modules, Convolutional Block Attention This paper presents YOLOv8, a novel object detection algorithm that builds upon the advancements of previous iterations, aiming to further enhance performance and robustness, and is poised to address the evolving needs of computer vision systems. Download PDF In this paper, we presented a comprehensive analysis of YOLOv8, highlighting its architectural innovations, enhanced training methodologies, and significant performance improvements over previous versions like YOLOv5. The architecture of Yolov8 is shown in Fig. 8% AP | Find, read and cite all the research you PDF | The major problem in Thailand related to parking is time violation. Recently, the field of vehicle-mounted visual intelligence technology has witnessed this paper, unmanned aerial vehicle (UA V) RGB images and an improved YOLOv8 target detec tion network are used to enhance the recognition accuracy of maize tassels. We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for Compared to traditional methods, the proposed YOLOv8-Lite model exhibits higher accuracy and robustness in object detection tasks, better addressing the challenges in fields such as autonomous driving, and contributing to the advancement of intelligent transportation systems. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. Section 5 covers our experimental setup and result analysis. 5% and 98. Editor Ijasre This paper explains the architecture and working of YOLO algorithm for the purpose of detecting and classifying objects, trained on the classes from COCO dataset. Gao, “Simple . 7, 2023 View PDF HTML (experimental) Abstract: This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous iterations like YOLOv5. In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant The purpose of this research is to learn about the YOLOv8 architecture, its improvements over previous versions, the COCO data set's make-up and evaluation metrics, and their strengths and weaknesses. The paper delves into the architecture of YOLOv8 and explores image 1)This work employs four different attention modules to the YOLOv8 architecture and proposes the YOLOv8-AM model for fracture detection, where the YOLOv8-AM model based on ResBlock + CBAM (ResCBAM) achieves the state-of-the-art (SOTA) performance. Specifically, we respectively employ four attention modules, Convolutional Block Attention Module (CBAM), Global Attention Mechanism (GAM), Efficient Channel Attention (ECA), and Shuffle Attention (SA), to design the improved The paper reviews YOLOv8's performance across benchmarks like Microsoft COCO and Roboflow 100, highlighting its high accuracy and real-time capabilities across diverse hardware platforms. Li, and Y. This paper This research work proposes YOLOv8-AM, which incorporates the attention mechanism into the original YOLOv8 architecture. pp5244-5252 Corpus ID: 271832893; A novel YOLOv8 architecture for human activity recognition of occluded pedestrians @article{Rajakumar2024ANY, title={A novel YOLOv8 architecture for human activity recognition of occluded pedestrians}, author={Shaamili Rajakumar and Ruhan Bevi Azad}, journal={International Journal of So, this paper proposes a WD model using PELSF-DCNN. Thus, we provide an in-depth explanation of the new architecture and functionality that YOLOv8 has PDF | Potholes pose a significant threat on roads, being a leading cause of accidents. In tasks that require ship detection The paper reviews YOLOv8’s performance across benchmarks like Microsoft COCO and Roboflow 100, highlighting its high accuracy and real-time capabilities across diverse hardware The architecture of YOLOv8 is structured around three core components: Backbone YOLOv8 employs a sophisticated convolutional neural network (CNN) backbone designed 2. 1007/s11042-023-17838-w Corpus ID: 266624530; An improved YOLOv8 for foreign object debris detection with optimized architecture for small objects @article{Farooq2023AnIY, title={An improved YOLOv8 for foreign object debris detection with optimized architecture for small objects}, author={Javaria Farooq and Muhammad Muaz and shows the YOLOv2 architecture. Written by Vindya Lenawala. Confusion Matrix PDF | This paper presents a comprehensive comparative analysis of the YOLOv8 object detection architecture and its two novel variations: | Find, read and cite all the research you need on In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant attention for their speed and accuracy in real-time applications. The main contributions of this paper are as follows: • This work employs four different attention modules to the YOLOv8 architecture and proposes the YOLOv8-AM model for fracture detection, where the YOLOv8-AM model based on ResBlock + CBAM (ResCBAM) achieves the state-of-the-art (SOTA) performance. Khare and 3 other authors YOLOv5 and YOLOv7, is conducted, emphasizing the importance of computational efficiency in various applications. Yolo, a scaled down version of the The structure of the remaining sections of this paper is as follows: Section 2 discusses related work. 5% and 0. The model framework's robustness is evaluated using YouTube video sequences with PDF | On Jan 1, 2020, Maria Kalinina and others published Research of YOLO Architecture Models in Book Detection | Find, read and cite all the research you need on ResearchGate This research work proposes YOLOv8-AM, which incorporates the attention mechanism into the original YOLOv8 architecture, and employs four attention modules, Convolutional Block Attention Module (CBAM), Global Attention Mechanism (GAM), Efficient Channel Attention (ECA), and Shuffle Attention (SA), to design the improved models and train YOLOv8 Architecture Explained stands as a testament to the continuous evolution and innovation in the field of computer vision. The GFLOPs of YOLOv8-HD decrease by 16%. Experimental results on a classroom detection dataset demonstrate that the improved model in this paper exhibits better detection performance compared to the original YOLOv8, with an average Conference Paper PDF Available. This research paper presents an approach that addresses the challenge of devising a proficient object detection and tracking system for a robotic agent to track individuals by amalgamating the This paper introduces an improved YOLOv8-based underwater object detection framework designed to address the challenges posed by the underwater environment, including noise, blur, colour . The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of 88. YOLOv8’s integration of the CSPNet backbone and the enhanced FPN+PAN neck has markedly improved feature extraction and DOI: 10. This paper introduces a frame-by-frame evaluation with respect to time, which is a. II. . Object recognition technology is an important technology used to judge the object’s The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. With its advanced architecture and cutting-edge algorithms, YOLOv8 has revolutionized the field of object detection, enabling accurate and efficient detection of objects in real-time scenarios. In this paper, the HR-YOLOv8 architecture is 3. This paper proposes a novel approach of bounding View PDF HTML (experimental) Abstract: This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous iterations like YOLOv5. 2. YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. An insulator defect detection algorithm based on an improved YOLOv8s model is proposed, with excellent performance in drone aerial photography for insulator defect detection and an improved loss function using SIoU is adopted to optimize the model's detection performance and enhance its feature extraction capability for insulator defects. Inspired by the detection in computer vision. YOLOv8 is a state-of-the-art deep learning model designed for real-time object detection in computer vision applications. Yolov8. These Paper tables with annotated results for YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes is conducted, emphasizing the importance of computational efficiency in various applications. Experimental results and the-Shelf_2014_CVPR_paper. 7717/peerjcs. The detection head, which is made up of the final 4 convolutional layers and the pass through layer that reorganizes the features of the 17 output of 26x26 x512 into PDF | Pathologists use histopathological images to diagnose cancer, and one key step in this process is to detect and examine the mitotic cells. To learn more about this topic, check out this YouTube video. YOLO variants are underpinned by the principle of real-time and high-classification performance, based on limited but efficient computational parameters. The This study presents a computer vision-based solution using YOLO for real-time helmet detection, leveraging the SHEL5K dataset, and proposes the CIB-SE-YOLOv8 model, which incorporates SE attention mechanisms and modified C2f blocks, enhancing detection accuracy and efficiency. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in 1. The application of human detection in pedestrian areas using aerial image data is used as the dataset in the deep learning input process and YOLOv8 outperforms Y OLOv5 when both architecture performances are applied. This paper provides a comprehensive survey of Through comprehensive testing on diverse surveillance videos, this paper validate YOLOv8's enhanced performance and efficiency in recognizing human postures and actions and underscores YOLOv8’s significant practical This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous This paper discuss about the YOLOv8 model to confirm its overall applicability, on two datasets namely FDDB & MASK. Backbone. The achieved performance of YOLOv8 is a precision of 84. 2020 6th International Today's deep learning methods focus on how to design the most appropriate objective functions so that the prediction results of the model can be closest to the ground truth. Finally, Section 6 concludes the paper. 5% enhancement over the original YOLOv8 architecture and underscores the effectiveness of our approach in the automatic visual inspection of miniature capacitors. Important improvements on the input side are adaptive grayscale filling, adaptive anchor frame computation, and mosaic data augmentation. The paper in [12] presented the Lightweight SM-YOLOv5 algorithm for tomato fruit detection in plant factories. The four primary tasks supported by YOLOv8 are pose estimation, categorization, object identification, and instance segmentation. Artificially created rainy (B), hazy (C) and low-light (D) images. Ensuring safety on construction sites is critical, with helmets playing a This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors and identifies the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. the YOLOv8 architecture. Meanwhile, an appropriate architecture that can facilitate acquisition of enough information for prediction has to be designed. A comprehensive analysis of YOLO’s evolution is presented, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. networks is unnecessary. Additionally, the study explores YOLOv8's developer-friendly enhancements, such as its unified Python package and CLI, which streamline model training and This paper presents a generalized model for real-time detection of flying objects that can be used for transfer learning and further research, as well as a refined model that achieves state-of-the-art results for flying object detection. 5 This paper provides a thorough analysis and comparison of the YOLOv5 and YOLOv8 models for wildfire and smoke detection, using the Foggia dataset for evaluation. These models are designed to cater to various requirements, from object detection to more complex tasks like instance segmentation, pose/keypoints detection, oriented object detection, and classification. The inference time of YOLOv8-HD is 2. Section 2 is Experimental results show that YOLOv8-DEE achieves a mean average precision (mAP) of 97. A novel YOLOv8 architecture for human activity recognition of (Shaamili Rajakumar) 5248 ISSN: 2088-8708 Figure 2. 94% on the validation dataset and 81. YOLOv8. Resource Link. Through the the effectiveness of detection without delay a unified architecture moves incredibly quickly and as well 45 frames per 2D frame yolo model process image in real time. Key innovations, including the CSPNet backbone for enhanced feature extraction, the FPN+PAN neck for superior multi YOLOv8 oers ve variants, the smallest comprising 225 layers. 86 ms (on GPU), which is lower than The introduction of a lightweight convolution SEConv in lieu of standard convolutions reduces the network’s parameter count, thereby expediting the detection process, and an ingenious Efficient Multi-Scale Attention Exploring YOLOv8 architecture applications for weed detection in crops AleksandarPetrovic1* ,Milos Pavkovic1,MarinaSvicevic2,Nebojsa Budimirovic 1,VukGajic ,andDejan Jovanovic3 The structure of the paper is provided in the following text. Each variant of the YOLOv8 series is optimized for its First, in view of the common problem that small targets in aerial images are prone to misdetection and missed detection, the idea of Bi-PAN-FPN is introduced to improve the neck part in YOLOv8-s. Anchor-free Split Ultralytics Head: YOLOv8 adopts an anchor-free split Ultralytics head, which contributes to The BiFPN module generates three feature images and blends them using adaptive weighting. This study focuses on pruning the YOLOv8 model's architecture, particularly the P5 head section, which detects larger objects, and makes the model faster and lighter, making it suitable for real-time surveillance. 2. Unlike the commonly used YOLOv8 method, an attention mechanism named MHSA and a The architecture has a significant advantage in the area of computational complexity: on the one hand, the Bo leneckCSP architecture reduces complexity and improves speed in the inference process describes the general yolov8 architecture Fig 2 – yolov8 Model 4. YOLOv8 introduces improvements in the form of a new neural network architecture [11]. Accuracy improvement: A paramount objective of this research revolves around accentuating the accuracy of object detection in YOLOv8, with a spotlight on scenarios encapsulating small objects or objects exhibiting complex geometrical shapes []. In this paper, we introduce six modied versions of YOLOv8 tailored for dierent object sizes: small, medium, large, small–medium, medium–large, and small–large. the YOLOv8 architecture has undergone a number of modifications and new convolutions: [35 37 49] https The network structure proposed in the referenced paper combines multi-branch architecture, re-parameterization techniques, and lightweight design principles to enhance network detection performance without significantly increasing inference time. The development of UAV technology has reached the stage of implementing artificial intelligence, control, and sensing. Cameras as UAV data inputs Figure 3 — Example of Stride. 62%, recall of 75. YOLOv8 uses a Darknet variation called CSPDarknet53 as its foundation.
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