Title: Automatic defect inspection system for beer bottles based on deep residual learning

Authors: Qiaokang Liang; Shao Xiang; Jianyong Long; Dan Zhang; Gianmarc Coppola; Wei Sun; Yaonan Wang

Addresses: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha 410082, China; National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China ' College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha 410082, China; National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China ' College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha 410082, China; National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China ' Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario, L1H 7K4, Canada ' College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha 410082, China; National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China ' College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha 410082, China

Abstract: Automatic detection of defects in recyclable beer bottles would reduce both the cost of the production process and the time spent in the quality inspection. A novel approach is proposed for automatic detection of defects occurring on the beer bottles by deep residual learning. This method extracts the characteristic information of beer bottle defects through the deep learning network and realises the classification of defect characters. In this work, the recognition of three kinds of common defects (defective body, defective mouth, and defective bottom) is realised, and the promising result demonstrated that the proposed method is capable of inspecting defects of beer bottles with outstanding accuracy. Particularly, a state-of-the-art convolutional neural network (CNN) was applied to the detection of beer bottle defects, which improved the accuracy of beer bottle detection comparing with traditional methods. Experimental results show that the new approach satisfies the requirement of defect detection and is able to improve the production efficiency.

Keywords: detection of defects; deep learning; convolutional neural network; CNN; quality inspection.

DOI: 10.1504/IJCVR.2021.115162

International Journal of Computational Vision and Robotics, 2021 Vol.11 No.3, pp.299 - 314

Received: 02 Apr 2019
Accepted: 13 Dec 2019
Published online: 21 May 2021 *

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