Lightweight pothole detection method based on improved YOLOv5
Author:
Affiliation:
1. Chongqing University of Posts and Telecommunications,School of Computer Science and Technology,Chongqing,China,400065
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10164832/10164812/10165578.pdf?arnumber=10165578
Reference18 articles.
1. Nuts&bolts: YOLO-v5 and image processing based component identification system
2. Yolov4: Optimal speed and accuracy of object detection[J];bochkovskiy;arXiv preprint arXiv 2004 10934,2020
3. SimAM: A simple, parameter-free attention module for convolutional neural networks[C];yang;International Conference on Machine Learning,2021
4. Annotated Potholes Image Dataset;rahman,2020
5. Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection
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