A Lightweight Appearance Quality Assessment System Based on Parallel Deep Learning for Painted Car Body

Abstract

The appearance quality assessment based on defect inspection for painted car-body surfaces is an essential work to monitor and analyze the level of paint appearance quality. In the industrial application, there are some challenges, such as the huge and stereo skeleton of car bodies, a variety of irregular local surface areas, low visibility of defects due to tiny real size, and specular car-body surface. To overcome these problems, a lightweight online appearance quality assessment system (OAQAS) based on parallel deep learning is proposed, it includes two parts: 1) a vision inspection subsystem with distributed multi-camera image acquisition module and 2) an appearance quality evaluation subsystem (AQES) based on parallel TinyDefectRNet for evaluating the proposed painted surface grinding difficulty criteria. TinyDefectRNet is able to inspect relatively accurate defect size, although it is trained on a coarsely annotated data set. The OAQAS is implemented in an actual painting production line of a car factory, and the application results show that our OAQAS is far superior to the manual inspection in evaluation accuracy and time consumption. Moreover, our system is lightweight so that it is easy to be plugged into existing painting production lines without rebuilding or changing the inspection room.