Yolov2 Implementation and Optimization for Moroccan Traffic Sign Detection

Taouqi, Imane; Klilou, Abdessamad; Chaji, Kebir; Arsalane, Assia

doi:10.1007/978-3-031-26254-8_122

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…YOLOv2 (YOLO9000) improved upon this by introducing Anchor Boxes and adopting the Darknet-19 backbone network, enhancing the detection of small objects. However, it still faced challenges in handling occlusion and multi-scale objects [32]. YOLOv3 further improved performance by introducing more anchor boxes and multi-scale detection to enhance detection accuracy [33].…”

Section: Research On One-stage Approaches In Object Detectionmentioning

confidence: 99%

“…YOLOv1 [31] The first object detection algorithm based on regression analysis Handling absolute positions is relatively challenging, leading to missed detections, and it struggles with recognizing small objects and exhibits poor robustness across different types of targets YOLOv2 [32] Model training has become more stable, and the model's robustness has been improved.…”

Section: Model Pros Consmentioning

confidence: 99%

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Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Liu,

Lin

2023

Electronics

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Intelligent traffic systems represent one of the crucial domains in today’s world, aiming to enhance traffic management efficiency and road safety. However, current intelligent traffic systems still face various challenges, particularly in the realm of target detection. These challenges include adapting to complex traffic scenarios and the lack of precise detection for multiple objects. To address these issues, we propose an innovative approach known as YOLOv8-SnakeVision. This method introduces Dynamic Snake Convolution, Context Aggregation Attention Mechanisms, and the Wise-IoU strategy within the YOLOv8 framework to enhance target detection performance. Dynamic Snake Convolution assists in accurately capturing complex object shapes and features, especially in cases of target occlusion or overlap. The Context Aggregation Attention Mechanisms allow the model to better focus on critical image regions and effectively integrate information, thus improving its ability to recognize obscured targets, small objects, and complex patterns. The Wise-IoU strategy combines dynamic non-monotonic focusing mechanisms, aiming to more precisely regress target bounding boxes, particularly for low-quality examples. We validate our approach on the BDD100K and NEXET datasets. Experimental results demonstrate that YOLOv8-SnakeVision excels in various complex road traffic scenarios. It not only enhances small object detection but also strengthens the ability to recognize multiple targets. This innovative method provides robust support for the development of intelligent traffic systems and holds the promise of achieving further breakthroughs in future applications.

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Section: Research On One-stage Approaches In Object Detectionmentioning

confidence: 99%

Section: Model Pros Consmentioning

confidence: 99%

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Liu,

Lin

2023

Electronics

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YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety

Jun,

Lai,

et al. 2024

Preprint

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With the rapid development of urbanization, the role of urban transportation systems has become increasingly prominent. However, traditional methods of traffic management are struggling to cope with the growing demands of traffic and the complexity of urban environments. In response to this situation, we propose the YOLOv8-BCC algorithm to address existing shortcomings. Leveraging advanced technologies such as CFNet, CBAM attention modules, and BIFPN structure, our algorithm aims to enhance the accuracy, real-time performance, and adaptability of urban traffic intelligent detection systems. Experimental results demonstrate significant improvements in detection accuracy and real-time performance compared to traditional methods. The introduction of the YOLOv8-BCC algorithm provides a robust solution for enhancing urban traffic safety and intelligent management.

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Yolov2 Implementation and Optimization for Moroccan Traffic Sign Detection

Cited by 2 publications

References 9 publications

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety

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