A YOLOX Object Detection Algorithm Based on Bidirectional Cross-scale Path Aggregation

To solve the problem of insufficient feature fusion between the deep and shallow feature layers of the original YOLOX algorithm, which resulting in a loss of object semantic information, this paper proposes a YOLOX object detection algorithm based on attention and bidirectional cross-scale path aggregation. First, an efficient channel attention module is embedded in the YOLOX backbone network to reinforce the key features in the object region by distinguishing between the importance of the different channels in the feature layer, thus enhancing the detection accuracy of the network. Second, a bidirectional cross-scale path aggregation network is designed to change the information fusion circulation path while increasing the cross-scale connections. Weighted feature fusion is used to learn the importance of the different path input features for differentiated fusion, thereby improving the feature information fusion capability between the deep and shallow layers. Finally, the SIOU loss function is introduced to improve the detection performance of the network. The experimental results show that on the PASCAL VOC2007 and MS COCO2017 datasets, the algorithm in this paper improves mAP by 2.32% and 1.53% compared with the original YOLOX algorithm, and has comprehensive performance advantages compared with other algorithms. The mAP reaches 99.44% on the self-built iron ore metal foreign matter dataset, with a recognition speed of 56.90 frames/s.