Citation

Chan, Wei Fun and Ang, Ee Mae (2025) Evaluate Road Depressions with Deep Learning and Mobile Application. In: 4th International Conference on Smart Cities, Automation, and Intelligent Computing Systems, ICON-SONICS 2025, 14 October 2025 - 17 October 2025, Hybrid, Malacca.

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Abstract

Road damage such as potholes and cracks potentially poses significant safety challenges, economic outlays, ecological impact, and governance concerns. This study presents an integrated approach using deep learning and mobile technology to automate the detection and severity classification of road depressions. The system compares three YOLO models, which are the YOLOv5s, YOLOv8s, and YOLOv11s, trained on the RDD2022 dataset to identify the optimal model for deployment. YOLOv8 is selected based on its balanced performance, achieving 65 percent precision, 59 percent recall, and 62 percent F1-score. A React Native mobile app enables users to upload images and receive feedback, while a Django backend handles prediction and status updates. This solution improves road maintenance efficiency and promotes public participation in infrastructure reporting.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	YOLO, road depression, deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 07:59
Last Modified:	19 Mar 2026 00:38
URII:	http://shdl.mmu.edu.my/id/eprint/15567

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