Federated Learning-Based Road Defect Detection with Transformer Models for Real-Time Monitoring

This research article presents a novel road defect detection methodology that integrates deep learning techniques and a federated learning approach. Existing road defect detection systems heavily rely on manual inspection and sensor-based techniques, which are prone to errors. To overcome these limitations, a data-acquisition system utilizing a GoPro HERO 9 camera was used to capture high-quality videos and images of road surfaces. A comprehensive dataset consist of multiple road defects, such as cracks, potholes, and uneven surfaces, that were pre-processed and augmented to prepare them for effective model training. A Real-Time Detection Transformer-based architecture model was used that achieved mAP50 of 99.60% and mAP50-95 of 99.55% in cross-validation of road defect detection and object detection tasks. Federated learning helped to train the model in a decentralized manner that enhanced data protection and scalability. The proposed system achieves higher detection accuracy for road defects by increasing speed and efficiency while enhancing scalability, which makes it a potential asset for real-time monitoring.