Improved YOLOv5s algorithm for aluminum sheet surface defect detection deployed on FPGA
Author:
Affiliation:
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, China
2. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3665065.3665074
Reference9 articles.
1. Research on Image-Based Automatic Wafer Surface Defect Detection Algorithm
2. Solar Cell Micro-Crack Detection Using Localised Texture Analysis
3. Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018).
4. Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020).
5. Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image
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