Fine Grained Image Recognition for Rail Surface Defects

Abstract:

Based on intelligent engineering technology， precise identification of track defects and quantified repair issues， combined with deep learning and computer vision techniques， a method for fine-grained image recognition and intelligent evaluation of rail surface defects has been proposed. Rail surface Defects dataset （named RD-1 094 dataset） was established by collecting images of rail surface defects and realizing fine-grained annotation of defects. The target density of the dataset reaches 22.9 targets （defects） per image. Moreover， a target detection algorithm with the ability of deep-learning for rail surface defects was established. Through training and learning to the RD-1094 Dataset， it achieved millimeter-level fine-grained recognition， such as realizing the recognition of spalling with size in 0.5 ~ 30 mm， corrugation with a wavelength in 20 ~200mm and other rail defects with their own growth situation. The algorithm has good generalization compatibility on single or double row corrugations， small or dense spallings in different levels， fatigue cracks with a single piece or with obscure distribution on the rail surface. It can measure the shape and localization of contact band on the rail surface， the size of the defects， the total number of defects in different levels， cracking area， the wavelength of corrugation and other quantitative evaluation metrics.