Abstract: As one of the important forms of equipment for underground auxiliary transportation in coal mines, monorail cranes have the advantages of large carrying capacity and strong climbing ability, and unmanned driving is its inevitable development direction in the context of intelligent construction of coal mines. In order to ensure the safe driving of the unmanned monorail crane in the underground roadway, it is particularly important for the reliable detection of track joints and key targets. Firstly, the mine image dataset was enhanced to improve its diversity. A YOLOv5 algorithm with improved channel attention mechanism ECA_s and EIOU regression loss function was proposed, and the improved YOLOv5 algorithm was experimentally analyzed, and the results showed that the recognition accuracy was increased by 9.1% and mAP by 3.6% by using the improved YOLOv5 algorithm. Secondly, a machine vision-based single-track crane track seam detection method was established, and the track seam distance was calculated according to the calibration coefficient by using image preprocessing, histogram information statistics, morphological processing and other technologies, and the results showed that the detection error of a seam image processed by the track seam detection algorithm based on machine vision was only 0.3 mm. Finally, the unmanned target detection test of the monorail crane is carried out, and the results show that the average accuracy of the improved YOLOv5 target detection algorithm reaches 90.3%, which has a higher detection accuracy. The average detection error of the seam image processed by the monorail crane rail seam detection algorithm is 0.73 mm, and the maximum detection error is not more than 1.1 mm, which has higher detection accuracy. In order to ensure the safe passage and reliability of unmanned monorail cranes in coal mines, a pragmatic and feasible detection scheme and an accurate and reliable detection algorithm are provided.