A deep-learning-based approach for fast and robust steel surface defects classification

elsevier journal 2019 Guizhong FuPeize Sun aWenbin ZhuJiangxin YangYanlong CaoMichael Ying YangYanpeng Cao

Automatic visual recognition of steel surface defects provides critical functionality to facilitate quality control of steel strip production. In this paper, we present a compact yet effective convolutional neural network (CNN) model, which emphasizes the training of low-level features and incorporates multiple receptive fields, to achieve fast and accurate steel surface defect classification... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Tasks


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.