Yet Another Deep Learning Approach for Road Damage Detection using Ensemble Learning

Abstract

For efficient road maintenance, an automated monitoring system is required to avoid laboriously and time-consuming manual inspection by road administration crews. One potential solution is to utilize image processing-based technologies, especially, as various sources of images have readily been available, e.g., surveillance cameras, in-vehicle cameras, or smartphones. Such image-based solutions enable detecting and classifying road damages. This paper introduces deep learning-based image analysis for road damage detection and classification. Our ensemble learning approaches with test time augmentation were thoroughly evaluated using the 2020 IEEE Big Data Global Road Damage Detection Challenge Dataset. Experimental results show that our approaches achieved an F1 score of up to 0.67, allowing us to win the Challenge.