Surface Defect Detection of Steel Products Based on Improved YOLOv5
Author:
Affiliation:
1. School of Electrical and Information Engineering, Tianjin University,Tianjin,China,300072
2. Hebei Jinxi Iron and Steel Group,Tangshan,China,063000
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9901509/9901494/09902037.pdf?arnumber=9902037
Reference21 articles.
1. SSD: Single shot multibox detector;liu;European Conference on Computer Vision,0
2. You Only Look Once: Unified, Real-Time Object Detection
3. YOLO9000: Better, Faster, Stronger
4. Yolov3: An incremental improvement;redmon;ArXiv,2018
5. YOLOv4: Optimal Speed and Accuracy of Object Detection;bochkovskiy;ArXiv,2020
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1. Steel Surface Defect Detection Using Improved Deep Learning Algorithm: ECA-SimSPPF-SIoU-Yolov5;IEEE Access;2024
2. Improved YOLOv3 Method for Detecting Surface Defects in Cold Rolled Steel Strip;2023 9th International Conference on Computer and Communications (ICCC);2023-12-08
3. Research on surface defect detection method of aluminum profile based on machine vision;2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS);2023-11-23
4. Real-Time Steel Surface Defect Detection with Improved Multi-Scale YOLO-v5;Processes;2023-04-28
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