Rail Image Harmonization Dataset: A Seed to Generate Evaluation Resources for Track Vision Inspection Systems

The track visual inspection system is a critical component in maintaining railway transportation safety. The scarcity of abnormal rail images presents a significant challenge for evaluating the performance of such inspection systems across diverse local railway divisions. Image harmonization emerges as a pivotal technique for generating evaluation resources for track vision inspection systems. However, the lack of suitable datasets has resulted in limited reporting on rail image harmonization techniques. This paper introduces the first Rail Image Harmonization Dataset (RHD). The dataset comprises 218 high-resolution rail images containing abnormal fasteners, captured by two inspection vehicles, and provides 14,712 pairs of inharmonious and harmonized rail image samples. Extensive experiments utilizing RHD were conducted, evaluating existing high-resolution harmonization methods alongside a specialized method termed Rail-DCCF -- a simplification of the state-of-the-art DCCF method. Comprehensive analyses of the RHD and the harmonization techniques employed in these methods are presented. RHD will provide fundamental data support for the research of rail image harmonization.