Abstract: With the deepening of metal open-pit mining,the road transportation conditions become more and more complex,and unmanned mining cards driving on the road faces the safety hazards of various obstacles,so the intelligent detection of obstacles for unmanned mine cards has put forward higher requirements. In this paper,a fusion of Swin Transformer and Convolutional Neural Network (CNN) for the intelligent detection of obstacles in front of open-pit mining trucks is proposed. The obstacle detection model needs to establish long-term dependencies to deal with increasing image data,and Swin Transformer can focus on global semantic information,which is beneficial to long-term modeling. The Swin Transformer is incorporated into the backbone feature extraction network of the YOLOX model to make full use of the multi-headed attention mechanism to preprocess image features,and the CBAM attention mechanism module is added to the enhanced feature extraction network to enable the model to extract more representational information in the subsequent feature extraction. The datasets used in the model are all from field mines and are pre-processed using data enhancement. After field mine data comparison and validation experiments,the results show that:the method can effectively identify unstructured road obstacles in metal open-pit mine with a complex background,and the detection accuracy reaches 91. 57% mAP,the detection speed reaches 56. 86 fps,with better small target and multi-scale target detection performance,which can meet the unmanned mine card in metal open pit with high accuracy detection requirements.

Key words: metal open-pit mine,unmanned mining cards,YOLOX,convolutional neural network,Swin Transformer,obstacle detection