The vehicle safety and stability during the driving process is directly influenced by the tire wear, and the vehicle safety can be improved by detecting the degree of tire wear to find the abnormal state of the tire. The tire wear degree can be detected with the sensor installed in the tire or the tire image directly. However, the sensor installed method has high cost and cumbersome installation process, and the image-based detection method requires more samples and the detection accuracy is not high. Therefore, a tire wear detection method based on fused texture features is proposed in this paper. Firstly, the training set was constructed using 25 tire images including 5 different wear degrees, and each image was uniformly cropped into 12 sub-images, and the gray level co-occurrence matrix and local binary patterns features were extracted by median filtering, and the fusion features were obtained by principal component analysis and stitching fusion method. Then, the classifier was trained by sparrow search algorithm and the random forest method with the fusion features. Finally, the algorithm was tested with 225 acquired images of tires with different degrees of wear. The results show that the average detection accuracy reaches 97.33%, which can meet the detection requirements of tire wear.