Road Damage Detection using Deep Ensemble Learning
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, an ensemble model for efficient detection and classification of road damages, which has been submitted to the IEEE BigData Cup Challenge 2020. The 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. The 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.
2020 IEEE International Conference on Big Data (Big Data)