Abstract
Rail surface defects seriously affect the safety of railway systems. At present, human inspection and rail vehicle inspection are the main approaches for the detection of rail surface defects. However, there are many shortcomings to these approaches, such as low efficiency, high cost, and so on. This paper presents a novel visual inspection approach based on unmanned aerial vehicle (UAV) images, and focuses on two key issues of UAV-based rail images: image enhancement and defects segmentation. With regards to the first aspect, a novel image enhancement algorithm named Local Weber-like Contrast (LWLC) is proposed to enhance rail images. The rail surface defects and backgrounds can be highlighted and homogenized under various sunlight intensity by LWLC, due to its illuminance independent, local nonlinear and other advantages. With regards to the second, a new threshold segmentation method named gray stretch maximum entropy (GSME) is presented in this paper. The proposed GSME method emphasizes gray stretch and de-noising on UAV-based rail images, and selects an optimal segmentation threshold for defects detection. Two visual comparison experiments were carried out to demonstrate the efficiency of the proposed methods. Finally, a quantitative comparison experiment shows the LWLC-GSME model achieves a recall of 93.75% for T-I defects and of 94.26% for T-II defects. Therefore, LWLC for image enhancement, in conjunction with GSME for defects segmentation, is efficient and feasible for the detection of rail surface defects based on UAV Images.
Highlights
Rail transportation plays a significant role in the development of economic and industrial growth, and the failures of railway facilities are directly related to catastrophic accidents [1]
The aircraft uses six Intelligent Flight Batteries to extend the time of flight
The built-in API Control feature, expandable center frame, and maximum takeoff weight of 15.1 kg make the Matrice 600 ideal for connecting other devices to meet the specific needs of different applications
Summary
Rail transportation plays a significant role in the development of economic and industrial growth, and the failures of railway facilities (such as defects on the rail surface) are directly related to catastrophic accidents [1]. Rail surface defects which include corrugations and discrete defects due to wheel-rail contact conditions are the most common forms of defects [2]. Those defects might cause serious accidents, or may even result in a catastrophic derailment of vehicles. This paper mainly discusses the detection of surface discrete defects
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