Indian Road Damage Detection Using Deep Learning

Abstract

Road maintenance technology is crucial for safe driving and accident prevention. Traditional methods using sensor-equipped trucks are costly for many local governments. Affordable devices like smartphones can scan road surfaces. Machine learning (ML) and deep learning (DL) models can effectively detect road damages. The authors used various versions of the “You Only Look Once” (YOLO) algorithm (YOLOv3, YOLOv5, YOLOv6, YOLOv7) with the Road Damage Detection 2020 (RDD 2020) dataset. Data augmentation through transformations and generative adversarial network (GAN) enhanced the dataset. DL models were trained using three methods: from scratch, transfer learning, and hyperparameter tuning. YOLOv6, trained with GAN-based augmentation and hyperparameter tuning, achieved the best results: 0.80 Precision, 0.85 Recall, 0.676 mAP@0.5, and 0.438 mAP@0.5:0.95. Dynamic range quantization reduced the model size by 75% without compromising accuracy. This study highlights YOLOv6 with GAN-based augmentation and hyperparameter tuning as a cost-effective road maintenance solution.