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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

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

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

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

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

Phased array ultrasonic S-scan testing of near-detection-surface defects based on a background subtraction algorithm

Materials Research Express, 2022
In phased array ultrasonic sector scanning testing, echoes from defects near the detection surface are often overlapped with interface echoes and cannot be characterized.
Yukuo Tian, Guocheng Xu, Lingbo Wei, Guanghao Zhou, Yuting Lin, Qiuyue Fan, Xiaopeng Gu   +6 more
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The Amalgamation of the Object Detection and Semantic Segmentation for Steel Surface Defect Detection

Applied Sciences, 2022
Steel surface defect detection is challenging because it contains various atypical defects. Many studies have attempted to detect metal surface defects using deep learning and had success in applying deep learning.
Mansi Sharma, Jongtae Lim, Hansung Lee
doaj   +1 more source

DG-GAN: A High Quality Defect Image Generation Method for Defect Detection

Sensors, 2023
The surface defect detection of industrial products has become a crucial link in industrial manufacturing. It has a series of chain effects on the control of product quality, the safety of the subsequent use of products, the reputation of products, and ...
Xiangjie He, Zhongqiang Luo, Quanyang Li, Hongbo Chen, Feng Li   +4 more
doaj   +1 more source