DCW-YOLO: Road Object Detection Algorithms for Autonomous Driving
Aiming at the problems of multiple parameters and poor detection accuracy of object detection network in automatic driving scenarios, an object detection algorithm based on improved YOLOv8 is proposed. First, a dynamic head framework is used to unify the object detection head and the attention mecha...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10431781/ |
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| Summary: | Aiming at the problems of multiple parameters and poor detection accuracy of object detection network in automatic driving scenarios, an object detection algorithm based on improved YOLOv8 is proposed. First, a dynamic head framework is used to unify the object detection head and the attention mechanism, and the attention mechanism is used for scale-awareness, spatial-awareness, and task-awareness, respectively, which significantly improves the representation capability of the object detection head without increasing the computational overhead. Second, the Coordinate Attention mechanism is embedded in the SPPF layer, which embeds the target’s location information into the channel attention to offer more precise localization for the model, suppress irrelevant aspects, and enable greater integration of local and global characteristics. Finally, the deleterious gradients generated by low-quality examples are reduced using the Wise-IoU v3 bounding box loss function in conjunction with a dynamic non-monotonic focusing mechanism utilizing an anchor box gradient gain assignment strategy. On the challenging public dataset KITTI, the accuracy is improved by 2.1% compared to the benchmark algorithm. In addition, the excellent performance on CCTSDB2021 and VOC highlights the generalization performance of the improved model. |
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| ISSN: | 2169-3536 |