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Non-Motor Vehicle Detection Model Based on YOLO Algorithm
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Shufeng Wang1, Qingwei Liang1, Yuhang Wang1, Qian Zhou2
Automotive Engineer | 2024, (8) : 8 - 14
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Automotive Engineer | 2024, (8): 8-14
• Special Issue on Intelligent Vehicle Environmental Perception and Target Detection Technology •
Non-Motor Vehicle Detection Model Based on YOLO Algorithm
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Shufeng Wang1, Qingwei Liang1, Yuhang Wang1, Qian Zhou2
Affiliations
  • 1 Shandong University of Science and Technology, Qingdao 266590
  • 2 BYD Auto Co., Ltd., Xi’an 710119
Published: 2024-08-15 doi: 10.20104/j.cnki.1674-6546.20240223
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To address the issue of false and missed detection of non-motorized vehicles due to the small size and obstructed vision in autonomous vehicle target detection, this research refines YOLOv4 basic algorithm to bolster the accuracy of non-motorized vehicle detection. The optimized algorithm streamlines the feature extraction process through a cross-stage connection, concurrently diminishing computational overhead and bolstering detection efficiency. Additionally, Convolutional Block Attention Module (CBAM) is embedded to increase effective feature weights and improve detection accuracy through channel and spatial attention weights. A non-motorized vehicle detection model is established based on anchor adaptive matching using a self-built non-motorized vehicle dataset. To verify the effectiveness of the model, the performance of the model is compared through ablation experiments. The results show that the proposed detection model substantially improves the detection and recognition performance of non-motor vehicles, effectively solve the problems of missed and false detections.

Non-motor vehicle detection  /  YOLOv4 algorithm  /  Convolutional Block Attention Module (CBAM)  /  Cross-stage connection  /  Ablation experiment
Shufeng Wang, Qingwei Liang, Yuhang Wang, Qian Zhou. Non-Motor Vehicle Detection Model Based on YOLO Algorithm[J]. Automotive Engineer, 2024 , (8) : 8 -14 . DOI: 10.20104/j.cnki.1674-6546.20240223
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Article Info
doi: 10.20104/j.cnki.1674-6546.20240223
  • Online Date：2025-11-25
  • Published：2024-08-15
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  • Revised：2024-07-11
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    1 Shandong University of Science and Technology, Qingdao 266590
    2 BYD Auto Co., Ltd., Xi’an 710119
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