The safety of automobile tires is crucial to passengers’ travel security. Abnormal tire wear is easy to cause a blowout, and serious wear threatens the life of passengers. Automated tire wear tests are necessary, since tire treads are mainly worn when cars run on the road. This research makes the automatic collection, transmission, and processing of tire images feasible, with the help of a Visual Studio 2017 development platform, C++ programming, and the OpenCV API interface of a computer vision library combined with the processing order of independent programming. The system can accurately extract tread images and determine the degree of tread wear through the characteristic values of gray level co-occurrence matrices of tread images, enabling the automatic detection of tread wear for automobile tires.