Automated visual inspection of angle cocks during train operation

Zhou, Fuqiang; Zou, Rong; Qiu, Yafei; He, Gaohong

doi:10.1177/0954409713495532

Cited by 28 publications

(25 citation statements)

References 23 publications

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“…Railway condition inspection routines are conducted periodically to ensure a proper condition of the structure or material (Márquez et al, 2010;Oukhellou et al, 2010;Zhou et al, 2013;2014). The traditional condition inspection process is performed by trained personnel who frequently searches for visual anomalies.…”

Section: Introductionmentioning

confidence: 99%

Real-time monitoring of brake shoe keys in freight cars

Zou

et al. 2015

Frontiers Inf Technol Electronic Eng

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Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key (BSK) is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent inspection process is divided into three stages: first, the region-of-interest (ROI) is segmented from the source image by an improved spatial pyramid matching scheme based on multi-scale census transform (MSCT). To localize the BSK in the ROI, census transform (CT) on gradient images is developed in the second stage. Then gradient encoding histogram (GEH) features and linear support vector machines (SVMs) are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2% and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.

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Section: Introductionmentioning

confidence: 99%

Real-time monitoring of brake shoe keys in freight cars

Zou

et al. 2015

Frontiers Inf Technol Electronic Eng

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“…Automated Non-Destructive Evaluation (NDE) using visual inspection was studied by [1] and finds applications in pipe inspection [2], train track inspection [3] and inspection of concrete structures [4]. The process usually employs a camera mounted on a mobile robot, automated vehicle or other apparatus which is moved over an area to be inspected while images are captured in quick succession.…”

Section: Introductionmentioning

confidence: 99%

Removal of specular reflections from image sequences using feature correspondences

Shah

Marshall

Murray

2017

Machine Vision and Applications

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Abstract. The presence of specular highlights can hide underlying features of a scene within an image and can be problematic in many application scenarios. In particular, this poses a significant challenge for applications where image stitching is used to create a single static image of a scene from inspection footage of pipes, gas tubes, train tracks and concrete structures. Furthermore, they can hide small defects in the images causing them to be missed during inspection. We present a method which exploits additional information in neighbouring frames from video footage to reduce specularity from each frame. The technique first automatically determines frames which contain overlapping regions before the relationship that exists between them is exploited in order to suppress the effects of specular reflections. This results in an image that is free from specular highlights provided there is at least one frame present in the sequence where a given pixel is present in a diffuse form. The method is shown to work well on greyscale as well as colour images and effectively reduces specularity and significantly improves the quality of the stitched image, even in the presence of noise. While applied to the challenge of reducing specularity in inspection videos, the method improves upon the state-of-the-art in specularity removal, and, its applications are wideranging as a general purpose pre-processing tool.

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“…So, for TFDS, fault recognition is accomplished automatically by computers or man-machines, which greatly enhances the efficiency and reliability of fault detection [5]. Generally, the main fault types include Center-Plate-Bolt losing, Safety Chain dropping, Brake-Shoe-Key losing, Side-Frame-Key losing and so on [5,6,7,8]. In these failures, the most common failure is Side-Frame-Key(SFK) losing fault.…”

Section: Introductionmentioning

confidence: 99%