Research progress of automated visual surface defect detection for industrial metal planar materials

X Fang, Q Luo, B Zhou, C Li, L Tian - Sensors, 2020‏ - mdpi.com
The computer-vision-based surface defect detection of metal planar materials is a research
hotspot in the field of metallurgical industry. The high standard of planar surface quality in …

[HTML][HTML] A review on rail defect detection systems based on wireless sensors

Y Zhao, Z Liu, D Yi, X Yu, X Sha, L Li, H Sun, Z Zhan… - Sensors, 2022‏ - mdpi.com
Small defects on the rails develop fast under the continuous load of passing trains, and this
may lead to train derailment and other disasters. In recent years, many types of wireless …

EDRNet: Encoder–decoder residual network for salient object detection of strip steel surface defects

G Song, K Song, Y Yan - IEEE Transactions on Instrumentation …, 2020‏ - ieeexplore.ieee.org
It is still a challenging task to detect the surface defects of strip steel due to its complex
variations, including variable defect types, cluttered background, low contrast, and noise …

Dense attention-guided cascaded network for salient object detection of strip steel surface defects

X Zhou, H Fang, Z Liu, B Zheng, Y Sun… - IEEE Transactions …, 2021‏ - ieeexplore.ieee.org
Recently, more and more researchers have paid attention to the surface defect detection of
strip steel. However, the performance of existing methods usually fails to detect the defect …

A nondestructive automatic defect detection method with pixelwise segmentation

L Yang, J Fan, B Huo, E Li, Y Liu - Knowledge-Based Systems, 2022‏ - Elsevier
Defect detection is essential for the quality control and repair decision-making of various
products. Due to collisions, uneven stress, welding parameters and other factors, cracks …

Unsupervised saliency detection of rail surface defects using stereoscopic images

M Niu, K Song, L Huang, Q Wang… - IEEE Transactions on …, 2020‏ - ieeexplore.ieee.org
Visual information is increasingly recognized as a useful method to detect rail surface
defects due to its high efficiency and stability. However, it cannot sufficiently detect a …

A rail surface defect detection method based on pyramid feature and lightweight convolutional neural network

Y Liu, H **ao, J Xu, J Zhao - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
The application of computer vision technology in defect detection of industrial products is a
popular research direction in recent years. This article presents the pyramid feature …

Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network

Y He, K Song, H Dong, Y Yan - Optics and Lasers in Engineering, 2019‏ - Elsevier
Defect inspection is very important for guaranteeing the surface quality of industrial steel
products, but related methods are based primarily on supervised learning which requires …

Attention network for rail surface defect detection via consistency of intersection-over-union (IoU)-guided center-point estimation

X Ni, Z Ma, J Liu, B Shi, H Liu - IEEE Transactions on Industrial …, 2021‏ - ieeexplore.ieee.org
Rail surface defect inspection based on machine vision faces challenges against the
complex background with interference and severe data imbalance. To meet these …

Multi-target defect identification for railway track line based on image processing and improved YOLOv3 model

X Wei, D Wei, D Suo, L Jia, Y Li - IEEE Access, 2020‏ - ieeexplore.ieee.org
The condition monitoring of railway track line is one of the essential tasks to ensure the
safety of the railway transportation system. Railway track line is mainly composed of tracks …