Road Rutting Detection using Deep Learning on Images

TL;DR

This paper introduces a new dataset and deep learning models for detecting road rutting, a less-studied form of road damage, achieving promising accuracy and setting benchmarks for future research.

Contribution

It provides the first comprehensive dataset with annotations for road rutting detection and evaluates object detection and segmentation models on this dataset.

Findings

YOLOX-s achieves mAP@IoU=0.5 of 61.6%

PSPNet (Resnet-50) achieves IoU of 54.69 and accuracy of 72.67

The dataset and results serve as benchmarks for future research

Abstract

Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs. Research on road damage detection using image processing techniques and deep learning are being actively conducted in the past few years. However, these researches are mostly focused on detection of cracks, potholes, and their variants. Very few research has been done on the detection of road rutting. This paper proposes a novel road rutting dataset comprising of 949 images and provides both object level and pixel level annotations. Object detection models and semantic segmentation models were deployed to detect road rutting on the proposed dataset, and quantitative and qualitative analysis of model predictions were done to evaluate model performance and identify challenges faced in the detection of road rutting using the proposed method. Object detection model YOLOX-s achieves mAP@IoU=0.5 of 61.6% and semantic segmentation model PSPNet (Resnet-50) achieves IoU of 54.69 and accuracy of 72.67, thus providing a benchmark accuracy for similar work in future. The proposed road rutting dataset and the results of our research study will help accelerate the research on detection of road rutting using deep learning.