Academic Journal of Materials & Chemistry, 2024, 5(3); doi: 10.25236/AJMC.2024.050317.
Siyi Wu1, Jiarui Li2, Zhongyi Zhao1, Zihao Wang3, Junxi Li4
1Nanjing University of Science and Technology, Nanjing, China
2Xian University of Technology, Xi’an, China
3Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation
4Herzen University, St. Petersburg, Russian Federation
In this study, we use a neural network approach instead of the traditional manual approach to solve the problem regarding the detection of defects on steel surfaces. We introduce several attention mechanisms to improve the Yolo v8 neural network trained on a steel defect detection dataset. The results show that our improved Yolo v8 model improves the robustness of the model more significantly and can detect more detailed steel surface defects.
Attention Mechanism; Object Detection; Steel Surface Defects
Siyi Wu, Jiarui Li, Zhongyi Zhao, Zihao Wang, Junxi Li. Steel surface defect detection based on improved YOLOv8 neural network. Academic Journal of Materials & Chemistry (2024) Vol. 5, Issue 3: 110-116. https://doi.org/10.25236/AJMC.2024.050317.
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