Abstract: Aiming at the difficulty of the train bottom parts detection, a detection algorithm based on OSE-dResnet network is proposed. In order to increase the richness and accuracy of feature extraction, feature extraction is enhanced by increasing cross-layer transmission, based on the Resnet50 network. Second, an OSEnet module is embedded into the feature extraction network to enhance beneficial feature channels with global features. Finally, the feature layers of different scales are fused to achieve the feature information complementation. The experimental results show that the algorithm combining the OSEnet module and the d-Resnet network has a good detection effect on the bottom parts of the train. The proposed algorithm is validated on the test datasets, and mAP reaches 98.77%.

Key words: deep learning, object detection, d-Resnet, OSEnet, muti-scale feature fusion