In order to address the issues of significant target scale variations and insufficient feature information in road scenarios, an enhanced model named SC-YOLO based on the YOLOv5s algorithm is proposed. The model utilizes the Swin Transformer Block to expand the receptive field and strengthen the feature extraction capability. Leveraging the ConvNeXt Block, the model promotes feature information fusion to mitigate missed detections and false positives. Experimental evaluation on the KITTI traffic object dataset demonstrates that the improved model yields notable enhancements in terms of mAP@50%, accuracy, and recall rate, with improvements of 6.2%, 8.5%, and 4.8%, respectively, compared to the original algorithm. These results affirm the effectiveness of the improved model in enhancing the detection performance of small targets in complex road scenarios.
In order to solve the problem of missing target detection in traffic road target detection task due to excessive change of target scale in complex environment. An improved object detection method based on YOLOv5 is proposed, which introduces Coordinate Attention (CA), it fuses the coordinate attention mechanism into the last layer of the backbone network C3 module and the Bottleneck module in the C3 module to enhance the feature extraction ability of the target. The experimental results show that the vehicle detection accuracy of the fusion model reaches 96.5% on KITTI data set. Compared with other models, it can effectively improve the problem that the target scale of traffic road changes greatly.
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