Abstract: At present, there are some problems in the identification of stones and other small obstacles in the track during the driving of unmanned underground electric locomotive in coal mines, such as slow detection speed, low detection precision, and easy to cause missing detection and wrong detection for overlapping objects. In order to solve the above problems, a multi-target detection model (SE-HDC-Mask R-CNN) for underground electric locomotive is proposed. The model is improved on the basis of Mask R-CNN. By embedding a squeeze-and-excitation (SE) module in the residual block of the backbone feature extraction network ResNet, the importance and interrelation of each channel are learned. The capability of feature selection and capture of the network is enhanced. The standard convolution with a kernel size of 3×3 in the residual block is replaced with hybrid dilated convolution (HDC). On the premise of not changing the size of the feature image and not increasing the amount of parameter calculation, the receptive field can be increased by increasing the distance between the values when the convolution kernel processes the data. The experimental result show that the SE-HDC-Mask R-CNN model can effectively extract track, electric locomotive, signal light, pedestrian and stone objects. The average precision rate on the multi scene operation data set of underground electric locomotive is 95.4%, the average mask segmentation precision is 88.1%, the average bound box intersection ratio is 91.7%, the three indicators are all improved by 0.5% compared with the Mask R-CNN model. The detection precision of signal light and stone (small objects) is improved by 0.7% and 4.1% respectively. The comprehensive performance of SE-HDC-Mask R-CNN model is better than that of YOLOV2, YOLOV3-Tiny, SSD and Faster R-CNN model. The SE-HDC-Mask R-CNN model can effectively solve the problem of missing detection of small objects. The SE-HDC-Mask R-CNN model can effectively realize object detection in coal roadway straight track, curved track, dark environment, multi-object overlapping and other scenarios. It has certain generalization capability and high robustness, and basically meets the requirements of unmanned electric locomotive obstacle detection.