Automatic and Efficient Metallic Surface Defect Detection Based on Key Pixel Point Locations

Abstract

Surface defect detection aims to accurately recognize and distinguish types of defects and plays a key role in many applications. However, most of the recent studies focus on specific scenario detection and do not fairly consider the balance between the speed and accuracy. In the paper, we propose a key pixel points location-oriented method to identify multiscale defects, with several important properties: 1) A real-time template matching-based model is designed to speed up the process by introducing the Gaussian operator; 2) An improved Hough-based model is used to achieve a higher detection precision by deep mining both incremental properties and parallel properties; 3) An adaptive filtering-based image preprocessing method is proposed to eliminate the interference of multiple types of clutters and noises. In the experiments, a mean average rate of 96% was achieved to detect and classify four types of common defects and the average time was reduced to 0.149s. Furthermore, we fully evaluate the proposed method on two public datasets collected in real production lines and compare the results with other state-of-the-art methods. The results show that the proposed method achieved better balanced performance in many real application scenarios.

Cylinder Liner Defect Detection and Classification based on Deep Learning

“…In other metal surface defect detection, the traditional machine vision method is more widely used. A multi-scale defect recognition method was proposed by Yu Jiahui et al which has good accuracy and detection speed [10]. Tian Hongzhi et al designed a micro defect detection system for grinding surface by combining plane illumination mode with multi angle illumination mode [11].…”