Automobile tire life prediction based on image processing and machine learning technology

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts automobiles, their wear causes a large number traffic accidents. Therefore, predicting tire life has one the key factors determining vehicle safety. This paper presents prediction method based on image processing machine learning. We first build original database as initial sample. Since there usually only few sample libraries in engineering practice, we propose new feature extraction expression that shows excellent performance for small database. extract texture features by using gray-gradient co-occurrence matrix (GGCM) Gauss-Markov random field (GMRF), classify extracted K-nearest neighbor (KNN) classifier. then conduct experiments predict automobile tires. The experimental results estimated mean average precision (MAP) confusion evaluation criteria. Finally, verify effectiveness accuracy proposed life. obtained expected to be used real-time life, thereby reducing tire-related