Improvements to YOLOv4 for Steel Surface Defect Detection
Author:
Affiliation:
1. School of Control Science and Engineering Tiangong University,Tianjin,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9931185/9931186/09931299.pdf?arnumber=9931299
Reference16 articles.
1. Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks;ren;Proceedings of the 2015 Advances in Neural Information Processing Systems,2015
2. You Only Look Once: Unified, Real-Time Object Detection
3. YOLO9000: Better, Faster, Stronger
4. Yolov3: an incremental improvement;redmon;ArXiv e-prints,2018
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