Design of an integrated model for pothole detection and repair optimization using multimodal transformers and hybrid deep learning

Abstract

The detection and timely repair of potholes are crucial for maintaining road safety and minimizing vehicle damage. However, existing methods often suffer from limitations such as reliance on single-modal data, poor generalization across diverse environments, and suboptimal resource management. To address these challenges, we propose a comprehensive framework for enhanced pothole detection and repair optimization using advanced deep learning techniques. Our approach integrates four key methodologies: Multimodal Enhanced Pothole Detection with Person-Level Data (M-E-Pot holeNet), Hybrid Machine Learning-Deep Learning for Classification (Hybrid-Pot holeNet), Deep Reinforcement Learning for Pot hole Detection and Repair Optimization (DRL-Pot holeOpt), and Transfer Learning for Pothole Detection in Diverse Environments (TL-Pot holeAdaptNet). M-E-Pot holeNet employs a Self-Supervised Multimodal Transformer (SSMT) to fuse camera, accelerometer, and crowdsourced smartphone data, achieving robust detection with a 97 % accuracy and under 2 % false positive rate. Hybrid-Pot holeNet combines Graph Attention Networks (GAT) and XGBoost, modeling spatial road features to classify potholes with 95 % accuracy and an F1-Score of 0.92. DRL-Pot holeOpt uses Soft Actor-Critic (SAC) with Bayesian Optimization to efficiently schedule repair tasks, reducing repair costs by up to 20 % and crew travel time by 15–25 %. Finally, TL-Pot holeAdaptNet leverages Domain-Adversarial Neural Networks (DANN) to ensure cross-domain adaptability, with 90 % accuracy in new environments and a 40–50 % reduction in domain discrepancy. This multi-faceted approach addresses the limitations of previous work by providing scalable, real-time, and resource-optimized solutions for pothole detection and maintenance, offering significant improvements in accuracy, cost efficiency, and adaptability.