A mobile vision inspection system for tiny defect detection on smooth car-body surfaces based on deep ensemble learning

• Published in: Measurement science & technology Vol. 30; no. 12; pp. 125905 - 125913
• Main Authors: Chang, Fei, Liu, Min, Dong, Mingyu, Duan, Yunqiang
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.12.2019
• Subjects: car-body surface; deep ensemble learning; mobile vision inspection system; tiny defect detection
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/ab1467

Defect inspection of car-body surfaces is a crucial step for assessing the appearance quality of car coatings. However, some problems such as uncertain imaging conditions, complicated local areas of car-body surfaces and tiny defects with ambiguous edges and low contrast in large images make defect detection more challenging. In order to overcome these challenges, a novel mobile inspection system based on computer vision technology is developed to inspect tiny defects on smooth car-body surfaces automatically. In our system, a dedicated image acquisition module (IAM) with a high-resolution camera, plane light-emitting diode light source and laser rangefinder is designed to capture surface images over bright field illumination. A specific deep ensemble learning algorithm, named TinyDefectNet, is proposed to identify tiny defects in large images acquired from the IAM online. The experiment results indicate our inspection system is on par with the average performance of experienced inspectors but it is much faster than artificial visual inspection. The developed inspection system has been successfully deployed in practice at a car coating plant and it has made inspection more efficient.