IVP-YOLOv5: an intelligent vehicle-pedestrian detection method based on YOLOv5s
Computer vision is now vital in intelligent vehicle environment perception systems. However, real-time small-scale pedestrian detection in intelligent vehicle environment perception systems is still needs to be improved. This paper proposes an intelligent vehicle-pedestrian detection method based on...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2168254 |
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| Summary: | Computer vision is now vital in intelligent vehicle environment perception systems. However, real-time small-scale pedestrian detection in intelligent vehicle environment perception systems is still needs to be improved. This paper proposes an intelligent vehicle-pedestrian detection method based on YOLOv5s, named IVP-YOLOv5, to use in vehicle environment perception systems. Based on the network structure of YOLOv5s, we replaced BottleNeck CSP with Ghost-Bottleneck to reduce the complexity of processing feature maps while maintaining good detection performance. To reduce the error between the ground truth box and the predicted box, we apply Alpha-IoU as the bounding box loss function, improving pedestrian detection accuracy and robustness. We introduce the slicing-aided hyper inference (SAHI) strategy, which enables the lightweight backbone network to capture more detailed features of pedestrians by enlarging image pixels. Experiments on the BDD100 K dataset show that the proposed IVP-YOLOv5 achieves 67.1% AP and 18.5% APs of pedestrian detection, and the GFLOPs and the number of parameters are only 10.5 and 4.9M. |
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| ISSN: | 0954-0091 1360-0494 |