Road Damage Detection using Deep Ensemble Learning

TL;DR

This paper introduces an ensemble deep learning model using YOLO-v4 for efficient detection and classification of road damages, achieving notable F1 scores in a competitive challenge, thus aiding road maintenance efforts.

Contribution

The work presents a novel ensemble approach combining multiple YOLO-v4 models for improved road damage detection and classification in diverse international datasets.

Findings

Achieved F1 scores of 0.628 and 0.6358 on two test datasets.

Demonstrated the effectiveness of ensemble models in road damage detection.

Validated the approach in the IEEE BigData Cup Challenge 2020.

Abstract

Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate efficient maintenance and resource management. In this work, we present an ensemble model for efficient detection and classification of road damages, which we have submitted to the IEEE BigData Cup Challenge 2020. Our solution utilizes a state-of-the-art object detector known as You Only Look Once (YOLO-v4), which is trained on images of various types of road damages from Czech, Japan and India. Our ensemble approach was extensively tested with several different model versions and it was able to achieve an F1 score of 0.628 on the test 1 dataset and 0.6358 on the test 2 dataset.