Steel Surface Defect Detection Method Based on Improved YOLOX
Author:
Affiliation:
1. Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, Guangdong, China
Funder
2021 Graduate Education Innovation Plan Project of Guangdong Province of China
2022 Guangdong Undergraduate Colleges and Universities Teaching Quality and Teaching Reform Project Construction
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10463029.pdf?arnumber=10463029
Reference41 articles.
1. Steel Surface Defect Detection Method and Application Based on Improved CenterNet
2. Steel Surface Defect Recognition: A Survey
3. Strip Steel Surface Defect Recognition Based on Novel Feature Extraction and Enhanced Least Squares Twin Support Vector Machine
4. Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates
5. Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion
Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. One improved YOLOX-s algorithm for lightweight section-steel surface defect detection;Advances in Mechanical Engineering;2024-08
2. Metal Structural Defect Detection Based-On Deep Learning and Grad-Cam;2024 International Conference on Circuit, Systems and Communication (ICCSC);2024-06-28
3. Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance;Sensors;2024-06-02
4. YOLOv7-WDD: An Efficient Bi-directional Feature Aggregation Method for Workpiece Defect Detection;2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI);2024-05-24
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