Intelligent Machine Vision for Detection of Steel Surface Defects with Deep Learning

EasyChair Preprint 10505

Abstract

With the high demand for steel surface quality, the requirement for defect-free steel surfaces is growing. Recent applications of deep learning in machine vision have demonstrated impressive performance. Our work aims to look into efficient surface defects detection algorithms, and to attempt to improve defect detection performance. This paper reports the use of YOLOv5 for steel surface defect detection and achieving 95.9% mean average precision(mAP). Moreover, we have improved detection accuracy by preprocessing the database with filters and denoisers based on CNNs. After applying denoisers and filters, apparent improvement can be seen in each type of defect after using either one of the techniques. For example, after applying denoisers and filters, the detection average precision(AP) of Rolled-in Scale defects increased by 12.6% and 35.4%, respectively. In this paper, the efficiency of machine vision based on deep learning, and the effectiveness of preprocessing in improving accuracy for steel surface defect detection are demonstrated.

Keyphrases: Denoising, deep learning, defect detection, machine vision

@booklet{EasyChair:10505,
  author    = {Yi Huang},
  title     = {Intelligent Machine Vision for Detection of Steel Surface Defects with Deep Learning},
  howpublished = {EasyChair Preprint 10505},
  year      = {EasyChair, 2023}}