Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network

By Jiangyun Li, Z. Su, Jiahui Geng, Yixin Yin. 2018

311 citations recorded by the source corpus.

Abstract The surface defects of steel strip have diverse and complex features, and surface defects caused by different production lines tend to have different characteristics. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. Aiming at detecting surface defects of steel strip, we established a dataset of six types of surface defects on cold-rolled steel strip and augmented it in order to reduce over-fitting. We improved the You Only Look Once (YOLO) network and made it all convolutional. Our improved network, which consists of 27 convolution layers, provides an end-to-end solution for the surface defects detection of steel strip. We evaluated the six types of defects with our network and reached performance of 97.55% mAP and 95.86% recall rate. Besides, our network achieves 99% detection rate with speed of 83 FPS, which provides methodological support for real-time surface defects detection of steel strip. It can also predict the location and size information of defect regions, which is of great significance for evaluating the quality of an entire steel strip production line.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/7d9ed36fc21b36ff0635a8ded4758b3a5598c3bf

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Industrial and Manufacturing Engineering

Citations in the corpus

A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2021, methodology): “The authors of [41] presented a method for detecting defects in steel strip surfaces, in real time, by using a modiﬁed version of the YOLO framework [7].”
A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond (2023, methodology): “The models have also been applied in surface inspection to detect defects and anomalies, enhancing quality control in manufacturing and production processes [26, 27, 28].”
Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020, methodology): “[110] improved the YOLO network by making it all convolutional and then applied the YOLO-variant to detect surface defects of flat steel.”
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS (2023, methodology): “The models have also been applied in surface inspection to detect defects and anomalies, enhancing quality control in manufacturing and production processes [36, 37, 38].”
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, methodology): “LeNet network structure can be used to detect defects in two situations: one is to design a complex multi-layer CNN structure, use different network structure to extra image content features, and complete end-to-end training to detect defects in images [56,83]; the other is to combine CNN with CRF m”