The wear detection of the guide pair (GP) plays a key role in the safe operation of the mine hoist system. Due to the actual working conditions of the well, manual detection is still the main detection method for GP wear, which has the problems of time consumption, low detection accuracy, and being unable to realize real-time detection. In view of this situation, this paper studies a machine vision-based wear detection method of GP in a mine hoisting system. First, the wear detection algorithm of GP is designed by means of image correction, image preprocessing, and edge extraction. Then, the hardware of the detection system is selected and designed, and the interface of the upper computer is designed by LABVIEW. Finally, according to the actual underground working conditions, a test platform for the wear detection system is built, and the detection experiment is carried out. The results show that the method can detect the wear and the location of the GP’s wear in real time. The maximum average error of the detection under three different wear conditions is 3.54%, which meets the requirements of the specified measurement accuracy. It can provide technical support for the automatic detection of the wear of GP in mine hoisting systems.