Multiple data sources and domain generalization learning method for road surface defect classification

TL;DR

This paper introduces a domain generalization method for road surface defect classification that effectively utilizes multiple data sources to create a model capable of generalizing to unseen datasets without retraining.

Contribution

The paper proposes a novel training scheme and domain generalization algorithm for multi-source data, enhancing the robustness of road defect classification models across different datasets.

Findings

Effective classification on unseen data sources

Utilized six international datasets for validation

Improved generalization performance over existing methods

Abstract

Roads are an essential mode of transportation, and maintaining them is critical to economic growth and citizen well-being. With the continued advancement of AI, road surface inspection based on camera images has recently been extensively researched and can be performed automatically. However, because almost all of the deep learning methods for detecting road surface defects were optimized for a specific dataset, they are difficult to apply to a new, previously unseen dataset. Furthermore, there is a lack of research on training an efficient model using multiple data sources. In this paper, we propose a method for classifying road surface defects using camera images. In our method, we propose a scheme for dealing with the invariance of multiple data sources while training a model on multiple data sources. Furthermore, we present a domain generalization training algorithm for developing a generalized model that can work with new, completely unseen data sources without requiring model updates. We validate our method using an experiment with six data sources corresponding to six countries from the RDD2022 dataset. The results show that our method can efficiently classify road surface defects on previously unseen data.