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

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.