Comparative Analysis of YOLOv8 and YOLOv10 in Vehicle Detection: Performance Metrics and Model Efficacy

Vehicles, Journal Year: 2024, Volume and Issue: 6(3), P. 1364 - 1382

Published: Aug. 10, 2024

Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis two advanced deep learning models—YOLOv8 YOLOv10—focusing on their efficacy in across multiple classes such as bicycles, buses, cars, motorcycles, trucks. Using range performance metrics, precision, recall, F1 score, detailed confusion matrices, we evaluate characteristics each model.The findings reveal that YOLOv10 generally outperformed YOLOv8, particularly detecting smaller more complex vehicles like bicycles trucks, which can be attributed to its architectural enhancements. Conversely, YOLOv8 showed slight advantage car detection, underscoring subtle differences feature processing between models. The buses motorcycles was comparable, indicating robust features both YOLO versions. research contributes field by delineating strengths limitations these models providing insights into practical applications real-world scenarios. It enhances understanding how different architectures optimized specific tasks, thus supporting development efficient precise systems.

Language: Английский

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InSAR-YOLOv8 for wide-area landslide detection in InSAR measurements

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 10, 2025

InSAR monitoring technology is widely used in investigating landslide hazards. Leveraging object detection algorithms to quickly extract information from Wide-Area measurements of great significance. Our InSAR-YOLOv8, an algorithm that automatically detects landslides measurements, addresses the low accuracy and suboptimal performance existing network models. In this method, we first design add a head specifically targeting small-scale objects. This improvement enhances model's ability features across different scales strengthens its capability detect varying sizes. We also replace original C2f module with lighter C2f_Faster process more efficiently, making model efficient. Finally, SIoU loss function replaces CIoU improve bounding box regression enhance accuracy. results show proposed achieves 97.41% mAP50, 66.47% mAP50:95, 92.06% F1 score on dataset, while reducing number parameters by 25%. Compared YOLOv8 other advanced models (YOLOvX, Faster R-CNN, etc.), our exhibits distinct advantages possesses wider range potential applications measurement for detection.

Language: Английский

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Classification of AO/OTA 31A/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques

Bone Reports, Journal Year: 2024, Volume and Issue: 22, P. 101801 - 101801

Published: Sept. 1, 2024

Language: Английский

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The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

Computers, Journal Year: 2024, Volume and Issue: 13(12), P. 336 - 336

Published: Dec. 14, 2024

This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, 11. As state-of-the-art model for object detection, has revolutionized field by achieving an optimal balance between speed and accuracy. The traces evolution variants, highlighting key architectural improvements, performance benchmarks, applications in domains such as healthcare, autonomous vehicles, robotics. It also evaluates framework’s strengths limitations practical scenarios, addressing challenges like small environmental variability, computational constraints. By synthesizing findings from recent research, this work identifies critical gaps literature outlines future directions enhance YOLO’s adaptability, robustness, integration into emerging technologies. researchers practitioners with valuable insights drive innovation detection related applications.

Language: Английский

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Road traffic accident detection based on Yolov8 and Byte Track

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3262, P. 040013 - 040013

Published: Jan. 1, 2025

Language: Английский

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BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 13, 2025

Accurate classification of cherry varieties is crucial for their economic value and market differentiation, yet genetic diversity visual similarity make manual identification challenging, hindering efficient agricultural trade practices. This study addresses this issue by proposing a novel deep learning-based hybrid model that integrates BiFPN with the YOLOv8n-cls framework, enhanced Swin Transformer Deformable Attention (DAT) techniques. The was trained evaluated on newly constructed dataset comprising from Turkey's Western Mediterranean region. Experimental results demonstrated effectiveness proposed approach, achieving precision 91.91%, recall 92.0%, F1-score 91.93%, an overall accuracy 91.714%. findings highlight model's potential to optimize harvest timing, ensure quality control, support export classification, thereby contributing improved practices outcomes.

Language: Английский

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ZeroEVNet: A multimodal zero-shot learning framework for scalable emergency vehicle detection

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126934 - 126934

Published: Feb. 1, 2025

Language: Английский

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Hybrid Deep Learning Aerial Framework for Road Scene Objects Segmentation and Classification

Published: Feb. 18, 2025

Language: Английский

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Improving Fire and Smoke Detection with You Only Look Once 11 and Multi-Scale Convolutional Attention

Fire, Journal Year: 2025, Volume and Issue: 8(5), P. 165 - 165

Published: April 22, 2025

Fires pose significant threats to human safety, health, and property. Traditional methods, with their inefficient use of features, struggle meet the demands fire detection. You Only Look Once (YOLO), as an efficient deep learning object detection framework, can rapidly locate identify smoke objects in visual images. However, research utilizing latest YOLO11 for remains sparse, addressing scale variability well practicality models continues be a focus. This study first compares classic YOLO series analyze its advantages tasks. Then, tackle challenges model practicality, we propose Multi-Scale Convolutional Attention (MSCA) mechanism, integrating it into create YOLO11s-MSCA. Experimental results show that outperforms other by balancing accuracy, speed, practicality. The YOLO11s-MSCA performs exceptionally on D-Fire dataset, improving overall accuracy 2.6% recognition 2.8%. demonstrates stronger ability small objects. Although remain handling occluded targets complex backgrounds, exhibits strong robustness generalization capabilities, maintaining performance complicated environments.

Language: Английский

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Enhanced Vehicle Identification Using YOLOv8 with Counter-Based Grouping for Improved Real-Time Performance

Algorithms for intelligent systems, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: Jan. 1, 2025

Language: Английский

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