From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection

Abstract

Monitoring road conditions is crucial for safe and efficient transportation infrastructure, but developing effective models for automatic road damage detection is challenging requiring large-scale annotated datasets. Cross-country collaboration provide access to diverse datasets and insights into factors affecting road damage detection models. This paper presents a review of winning strategies of the Crowdsensing-based Road Damage Detection Challenge (CRDDC) held in 2022 as a Big Data Cup, with 90+ teams from 20+ countries proposing solutions for six countries: India, Japan, the Czech Republic, Norway, the United States, and China. The best solution achieved an F1-score of 77 % for all six countries, which is 2.7 % better than the 2nd ranked solution. This study explores the impact of factors influencing dataset and model selection by CRDDC winners. The study’s insights can guide future research in making data-related choices and developing more effective road damage detection models accounting for the diverse road conditions across different countries.