Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets

TL;DR

This paper introduces a novel deep learning method combining YOLO and CBAM for accurate, real-time pavement distress detection tailored for developing countries, addressing unique environmental and resource challenges.

Contribution

It presents a new deep learning model specifically designed for local pavement distress detection, integrating attention mechanisms and a web app for practical deployment in resource-constrained settings.

Findings

High detection confidence scores (0.46 to 0.93) for multiple distress types

Effective detection of potholes, cracks, and raveling in diverse conditions

Insights into challenges of pavement assessment in developing countries

Abstract

Road infrastructure maintenance in developing countries faces unique challenges due to resource constraints and diverse environmental factors. This study addresses the critical need for efficient, accurate, and locally-relevant pavement distress detection methods in these regions. We present a novel deep learning approach combining YOLO (You Only Look Once) object detection models with a Convolutional Block Attention Module (CBAM) to simultaneously detect and classify multiple pavement distress types. The model demonstrates robust performance in detecting and classifying potholes, longitudinal cracks, alligator cracks, and raveling, with confidence scores ranging from 0.46 to 0.93. While some misclassifications occur in complex scenarios, these provide insights into unique challenges of pavement assessment in developing countries. Additionally, we developed a web-based application for real-time distress detection from images and videos. This research advances automated pavement distress detection and provides a tailored solution for developing countries, potentially improving road safety, optimizing maintenance strategies, and contributing to sustainable transportation infrastructure development.