@inproceedings{6e4fbe2d9a774582a21de0c41ac237d5,
title = "Pothole-YOLO: A Single-Stage Instance Segmentation Method for Pothole Detection",
abstract = "Potholes pose a significant hazard to traffic safety and transportation infrastructure. Traditional methods of human-based pothole detection present challenges in terms of cost and safety. However, instance segmentation technology offers a promising solution by accurately locating potholes and providing high-resolution features. This paper addresses the task of pothole detection by introducing a novel single-stage instance segmentation network called Pothole-YOLO. Our approach incorporates an efficient plug-and-play module called CEA-block to enhance the YOLO backbone and layers. Furthermore, we propose a robust and lightweight segmentation head named Pothole-Protonet to improve the segmentation prediction performance. Additionally, we enhance the pothole boundary features by modifying the Wise-IOU method, instead of using the common IOU method. Experimental results demonstrate that our Pothole-YOLO achieves the highest accuracy in pothole detection compared to other state-of-the-art methods on publicly available pothole datasets.",
keywords = "Deep learning, Instance segmentation, Pothole detection",
author = "Jintao Cheng and Xingming Chen and Weiwen Chen and Zhuoxu Huang and Jin Wu and Rui Fan and Xiaoyu Tang",
note = "Publisher Copyright: {\textcopyright} Beijing HIWING Scientific and Technological Information Institute 2024.; 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 ; Conference date: 09-09-2023 Through 11-09-2023",
year = "2024",
doi = "10.1007/978-981-97-1087-4_43",
language = "English",
isbn = "9789819710867",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Nature",
pages = "450--465",
editor = "Yi Qu and Mancang Gu and Yifeng Niu and Wenxing Fu",
booktitle = "Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) - Volume III",
address = "Switzerland",
}