A Low-Cost Multimodal Pothole Detection Framework Using YOLO-Based Vision and Infrared Depth Sensing for Road Infrastructure Maintenance
Yasheena Niromi Kaumini
Department of Information and Communication Technology, Faculty of Humanities and Social Sciences, University of Sri Jayewardenepura, Gangodawila, Nugegoda, Sri Lanka.
Maheesha Dhashantha Silva
*
Department of Information and Communication Technology, Faculty of Humanities and Social Sciences, University of Sri Jayewardenepura, Gangodawila, Nugegoda, Sri Lanka.
*Author to whom correspondence should be addressed.
Abstract
Aims: Road potholes are a leading cause of vehicle damage, reduced road safety, and increased accident risk, particularly in developing countries where road maintenance resources are limited. Existing automated detection systems rely predominantly on single-modality approaches, either vision-based or sensor-based, that cannot simultaneously localise potholes spatially and estimate their physical depth. This study presents the first low-cost, edge-deployed multimodal pothole detection framework that combines a deep learning visual detector with an analogue infrared sensor array for real-time depth estimation on commodity embedded hardware.
Methodology: A YOLOv12m object detection model was trained on 4,211 road images and paired with a 12-channel Sharp GP2Y0A21 infrared sensor array. Both subsystems were deployed on a Raspberry Pi 5 platform connected via two Arduino UNO microcontrollers. A decision-level weighted confidence fusion algorithm combined their outputs. An SVM classifier trained on 32 engineered IR features provided a four-level severity classification (None, Minor, Moderate, Severe). The SVM was evaluated using a corrected 5-fold stratified cross-validation pipeline to ensure leakage-free results.
Results: YOLOv12m achieved Precision = 1.0000(at the operational confidence threshold), Recall = 0.9952, and mAP@50 = 0.9950 at an inference latency of 4.13 ms per frame, outperforming six comparative YOLO architectures on all primary metrics. The SVM severity classifier achieved cross-validated accuracy = 0.9900, F1-score = 0.9831, and ROC-AUC = 1.000 across five folds. The fused system operates at 25 Hz on the Raspberry Pi 5 platform.
Conclusion: To the best of our knowledge, no prior published work has combined a spatially distributed multi-channel analogue infrared sensor array with a real-time YOLO visual detector on a single low-cost edge platform for simultaneous pothole localisation and physical severity estimation. The framework provides actionable, severity-graded outputs suitable for proactive road maintenance planning and directly contributes to reducing pothole-related road safety hazards.
Keywords: Pothole detection, YOLOv12, infrared depth sensing, multimodal fusion, severity classification, edge computing, road safety, intelligent transportation systems