Road Damage Detection and Classification using Deep Neural Network

Abstract

Road damage detection and classification is an important sector in computer vision and image processing. Road damage detection is rapidly getting a high application in various sectors of computer vision, especially in self-driving cars; it is getting great attention. We used the RDD-2020 dataset with four classes for training our models. Most of the existing methods using the RDD-2020 dataset failed to detect multiple overlapping damage regions of different classes in the same image. To overcome these limitations and enhance the previous model's performance, we experimented with five different pre-trained models. We fine-tuned the best-performing model (Faster RCNN with ResNet-101) by using three different optimizers (Adam, RMS Prop., and SGD with momentum) and kept batch sizes as 4, 8, 16, and 32. Then, we proposed a fine-tuned Faster RCNN with the ResNet-50 model. The experimented pre-trained models are EfficientDet, SSD with MobileNet-v1, SSD with MobileNetv2, SSD with ResNet-50, and Faster RCNN with ResNet-101. These models have a backbone network and a detecting head. The backbone network extracts the feature maps, and the detection head generates bounding box coordinates for detecting the damaged region and classifies these bounding boxes. We have found a maximum f1 score of 0.47 for the longitudinal class, 0.41 for transverse class, 0.41 for alligator class, and 0.35 for pothole using Fine-tuned Faster RCNN with ResNet-101 by keeping the batch size as 4 and optimizer as SGD with momentum which are better than the previous model of literature using RDD-2020 dataset.