Country-specific Ensemble Learning: A Deep Learning Approach for Road Damage Detection

Automated monitoring systems have been utilized for effective road maintenance in order to eliminate time-consuming and manual inspection by road administration employees. Image-based technology has grown as an important option since a variety of images, such as from surveillance cameras or on-dash cameras, are widely obtainable. Towards that solution, this paper introduces a deep learning-based approach for detecting and classifying road damages in diverse images collected from different countries. Our approach integrates individual models trained per country and a general model trained for all countries. Our approach were evaluated thoroughly using the 2022 IEEE BigData Crowdsensing-based Road Damage Detection Challenge (CRDDC) Datasets. Experimental results show that our approach achieved an F1 score of up to 0.73.