Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

By Xian Tao, Dapeng Zhang, Wenzhi Ma, Xilong Liu, De Xu. Applied Sciences, 2018

426 citations recorded by the source corpus.

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

Subfields: Computational Mechanics, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/10447a2738ff7ac47f8d93c85222ee737412cf53

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In Computational Mechanics

Citations in the corpus

Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020, background): “[116] proposed a novel cascaded AE (CASAE) framework to detect some complex defects under industrial environment, which converts test images into pixel-wise prediction mask based on semantic segmentation.”
Survey on semantic segmentation using deep learning techniques (2019, background): “It has several applications in computer vision & artiﬁcial intelligence − autonomous driving [2, 3], robot navigation [4], industrial inspection [5]; remote sensing [6]; In cognitive and computational sciences − saliency object detection [7, 8]; In Agriculture sciences [9]; Fashion − categorizing…”