Pothole-YOLO: A Single-Stage Instance Segmentation Method for Pothole Detection

Jintao Cheng, Xingming Chen, Weiwen Chen, Zhuoxu Huang, Jin Wu, Rui Fan, Xiaoyu Tang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


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.

Original languageEnglish
Title of host publicationProceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) - Volume III
EditorsYi Qu, Mancang Gu, Yifeng Niu, Wenxing Fu
PublisherSpringer Nature
Number of pages16
ISBN (Print)9789819710867
Publication statusPublished - 2024
Event3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Nanjing, China
Duration: 09 Sept 202311 Sept 2023

Publication series

NameLecture Notes in Electrical Engineering
Volume1173 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023
Period09 Sept 202311 Sept 2023


  • Deep learning
  • Instance segmentation
  • Pothole detection


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