Abstract: During the operation of unmanned electric locomotives in underground mines,potential collision objects such as personnel,ore,and equipment frequently appear,posing significant safety hazards to production and transportation.To miti gate such risks,the YOLOv7 model was selected for collision object identification with reference to similar cases.However,de tection results indicated that the YOLOv7 model suffered from issues in detection region partitioning and collision object feature extraction within the underground environment,leading to frequent missed and false detections.To address these deficiencies, the YOLOv7 model was improved by incorporating the SimAM attention mechanism to assist in detection region division and by replacing the original CIoU loss function with the EIoU loss function to enhance feature extraction capability.Experimental re sults demonstrate that the improved YOLOv7 model achieves a mean Average Precision (mAP) of 98.41%,which is 4.62 per centage points higher than that of the original YOLOv7 model,and the frame rate (Frames Per Second,FPS) is increased by 8 frames per second.Moreover,the precision and recall for detecting various types of collision objects are significantly improved. The enhanced model can effectively satisfy the requirements for potential collision detection in unmanned underground locomo tive operations,thereby providing technical support for the realization of fully autonomous underground haulage.

Key words: underground mine,unmanned underground driving,YOLOv7,deep learning,object detection