Object detection algorithm based on DSGIoU loss and dual branch coordinate attention

The bounding box regression loss effect is limited, and the multi-scale feature representation ability is insufficient in the YOLOX algorithm, which leads to inaccurate detection results. To address this issue, an object detection algorithm based on distance shape of generalized intersection over union (DSGIoU) loss and dual branch coordinate attention was proposed. Based on the intersection over union (IoU) loss term, the regression convergence effect of the bounding box was optimized by adding three penalty terms: non-overlapping area, distance from the center, and aspect ratio between the true box and the predicted box. Meanwhile, the feature was encoded in two directions by using average pooling and max pooling to obtain directional perception information and position information, so as to enhance the feature. To demonstrate the detection performance of the proposed algorithm, YOLOX with network sizes of Tiny, S, and M was used as the benchmark to carry out tests on PASCAL VOC and KITTI datasets. The experimental results show that the detection accuracy of the proposed algorithm on the PASCAL VOC dataset reaches 80.0%, 82.6%, and 85.8%, respectively, which is 1.5%, 1.6%, and 2.0% higher than the YOLOX as the benchmark. On the KITTI dataset, the detection accuracy reaches 87.7%, 89.7%, and 90.7%, which is increased by 1.7%, 2.9%, and 1.3%, respectively. The proposed algorithm can optimize the network convergence, improve the representation ability of multi-scale features, and significantly boost the detection accuracy.