Vehicle fault diagnosis with deep learning

amir mohammad moradi¹ Alireza taghipour²

1) Computer student at Tonkebon School in Iran email :
2) mechanic student at Tonkebon School in Iran email :

Publication : 7th International Conference on Applied Researches in Science & Engineering (7carse.com)
Abstract :
At present, automobiles have become a common means of transportation, but with the increase of vehicles, safety issues have gradually emerged. Therefore, the assembly, manufacture and production of vehicles require systematic testing and rigorous inspection. Therefore, defect detection of vehicle parts is particularly important. Vehicle parts defect detection has evolved from manual detection of traditional classification methods to machine vision methods. In this paper, the deep learning method is used to firstly detect the defects of vehicle parts through the training of VGG16 network structure model. The accuracy rate is 94.36. Secondly, the VGG16 network structure model is improved. By introducing the inceptionv3 module, the width of the model is increased on the basis of depth, the image is better recognized with an accuracy of 95.29. However, the accuracy of the traditional HOG+SVM classification method is only 93.88, and the efficiency of both methods is higher than that of the traditional method.
Keywords : Artificial Intelligence - Car Fault - Deep Learning