A Low-Cost Multimodal Pothole Detection Framework Using YOLO-Based Vision and Infrared Depth Sensing for Road Infrastructure Maintenance

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.