Design of auto-test system for vehicle intelligent surface based on deep learning
Traditional machine vision methods are susceptible to light source and ambient conditions when testing the defects in vehicle intelligent surface,resulting in feature extraction difficult,poor robustness and accuracy.In this paper,a collaborative robot is used as the motion control system,and the detection system is composed of a pressure detection unit,a current acquisition unit and a image detection unit.A method based on deep learning network of detecting the defects of intelligent surface is proposed.The accuracy of detecting and classification of defect has been reached 95％,improved 10％compared with the traditional methods.This system improved the automation level,the test accuracy and robustness greatly.
Deep learningIntelligent surfaceTestCollaborative robotImage processing
万方数据
维普
