Improved small object detection algorithm based on YOLOv5

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.