MS-YOLOv5s: An Improved YOLOv5s for the Detection of Imperceptible Defects on Steel Surfaces

Chenchen Wang, Mian Zhou*, Yuan Liang, Weiwei Pan, Zan Gao

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

The detection of surface defects in steel is crucial for maintaining high product quality standards and preventing financial losses due to inferior goods. However, the subtle imperfections that escape human observation present a significant challenge to existing algorithms. In response to this pressing need, our research introduces an advanced approach, MS-YOLOv5s, designed to better distinguish between background and defects. Firstly, our study presents a new neck module, the comprehensive position feature pyramid networks (CPFPN), which improves the precision of detecting barely noticeable flaws. This is achieved by using the spatial channel attention module (SCAM) on intermediate feature maps to retain more positional information from the original image. Moreover, this innovative method adopts multi-scale learning, dynamically adjusting the input image size during training to amplify the differences between defects and background. MS-YOLOv5s achieves 80.5% and 65.7% mean average precision (mAP) respectively on the NEU-DET and GC10-DET datasets, demonstrating robust performance across various scenarios and outperforming many methods in identifying defects on the steel surface.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Technology and Applications - 20th International Conference, ICIC 2024, Proceedings
EditorsDe-Shuang Huang, Chuanlei Zhang, Jiayang Guo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages404-415
Number of pages12
ISBN (Print)9789819756087
DOIs
Publication statusPublished - 2024
Event20th International Conference on Intelligent Computing, ICIC 2024 - Tianjin, China
Duration: 5 Aug 20248 Aug 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14871 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Intelligent Computing, ICIC 2024
Country/TerritoryChina
CityTianjin
Period5/08/248/08/24

Keywords

  • Comprehensive position feature pyramid networks
  • Multi-scale learning
  • Spatial channel attention module
  • Steel surfaces

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