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Sparse cross-transformer network for surface defect detection [PDF]

Scientific Reports
Quality control processes with automation ensure that customers receive defect-free products that meet their needs. However, the performance of real-world surface defect detection is often severely hindered by the scarcity of data.
Xiaohua Huang, Yang Li, Yongqiang Bao, Xiaochun Zhu   +3 more
doaj   +4 more sources

RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 [PDF]

Sensors
Surface defect detection in chips is crucial for ensuring product quality and reliability. This paper addresses the challenge of low identification accuracy in chip surface defect detection, which arises from the similarity of defect characteristics ...
Wenjie Tang, Yangjun Deng, Xu Luo
doaj   +2 more sources

Workpiece surface defect detection based on YOLOv11 and edge computing. [PDF]

PLoS ONE
The rapid development of modern industry has significantly raised the demand for workpieces. To ensure the quality of workpieces, workpiece surface defect detection has become an indispensable part of industrial production.
Zishuo Wang, Tao Ding, Shuning Liang, Hongwei Cui, Xingquan Gao   +4 more
doaj   +2 more sources

YOLOv8n-GSS-Based Surface Defect Detection Method of Bearing Ring [PDF]

Sensors
Industrial bearing surface defect detection faces challenges such as complex image backgrounds, multi-scale defects, and insufficient feature extraction.
Shijun Liang, Haitao Xu, Jingyu Liu, Junfeng Li, Haipeng Pan   +4 more
doaj   +2 more sources

Study on Novel Surface Defect Detection Methods for Aeroengine Turbine Blades Based on the LFD-YOLO Framework [PDF]

Sensors
This study proposes a novel defect detection method to address the low accuracy and insufficient efficiency encountered during surface defect detection on aeroengine turbine blades (ATBs). The proposed approach employs the LDconv model to adjust the size
Wei Deng, Guixiong Liu, Jun Meng
doaj   +2 more sources

Lightweight Industrial Products Defect Detection Network Based on Attention [PDF]

Jisuanji gongcheng, 2023
The detection of surface defects in industry is of great significance in improving the quality of industrial products and maintaining production safety. As surface defects are complex, diverse, and of different shapes, higher requirements are put forward
Gang LI, Rui SHAO, Mingle ZHOU, Min LI, Honglin WAN
doaj   +1 more source

A high-effective multitask surface defect detection method based on CBAM and atrous convolution

Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2022
Given the shortcomings of conventional machine vision-based surface defect detection methods, including their low accuracy, long development cycle, and poor generalization ability, this paper proposes a surface defect detection model based on the ...
Xin XIE, Lei XU, Xinlei LI, Bin WANG, Tiancheng WAN   +4 more
doaj   +1 more source

A pixel-wise framework based on convolutional neural network for surface defect detection

Mathematical Biosciences and Engineering, 2022
The automatic surface defect detection system supports the real-time surface defect detection by reducing the information and high-lighting the critical defect regions for high level image under-standing.
Guozhen Dong
doaj   +1 more source

Surface Defect Detection Methods for Industrial Products: A Review

Applied Sciences, 2021
The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products.
Yajun Chen, Yuanyuan Ding, Fan Zhao, Erhu Zhang, Zhangnan Wu, Linhao Shao   +5 more
doaj   +1 more source

Review of surface defect detection of steel products based on machine vision

IET Image Processing, 2023
Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades.
Bo Tang, Li Chen, Wei Sun, Zhong‐kang Lin   +3 more
doaj   +1 more source