Abnormal Target Detection of High-Speed Train's Roof

Song, Wenwei; Gao, Xiaorong; Peng, Jianping; Li, Jinlong; Xie, Liming

doi:10.1109/fendt.2017.8584563

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…Data Sets and Data Processing. With the assistance of our previous work [1], about 18k image pairs that are the corresponding parts of the same high-speed train were collected at different times. These images are all taken from the highspeed train's body and its key components, such as the locomotive running gear, bogie, wheel, fastening bolt, and pipeline.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Thus, we do not have a sufficient number of samples to implement deep learning to detect the abnormal targets. Instead, we devised a method based on the structural similarity method (SSIM) in the previous work [1]. In this method, the historical train images without malfunction are taken as baselines.…”

Section: Introductionmentioning

confidence: 99%

“…Although the previous work [1] saves plenty of manpower, there remains a need for inspectors to spend time classifying which changes are dangerous. To further reduce labor cost, this work is aimed at an automatic identification of the correct alarms with deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the manually designed descriptors, such as SIFT [2], may be not considered as an optimal method due to the factors mentioned above. On the other hand, it is effortless to obtain a large dataset that contains corresponding image pairs from the algorithm designed according to the previous work [1]. Therefore, deep learning [3] is adopted.…”

Section: Introductionmentioning

confidence: 99%

“…Multishape Training. The image pairs are directly provided by the previous work[1], and the shape is arbitrary. Namely, the height-width ratio is uncertain, for which our network should adapt to different shapes.…”

mentioning

confidence: 99%

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A Convolutional Neural Network‐Based Classification and Decision‐Making Model for Visible Defect Identification of High‐Speed Train Images

et al. 2021

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In high-speed train safety inspection, two changed images which are derived from corresponding parts of the same train and photographed at different times are needed to identify whether they are defects. The critical challenge of this change classification task is how to make a correct decision by using bitemporal images. In this paper, two convolutional neural networks are presented to perform this task. Distinct from traditional classification tasks which simply group each image into different categories, the two presented networks are capable of inherently detecting differences between two images and further identifying changes by using a pair of images. In doing so, even in the case that abnormal samples of specific components are unavailable in training, our networks remain capable to make inference as to whether they become abnormal using change information. This proposed method can be used for recognition or verification applications where decisions cannot be made with only one image (state). Equipped with deep learning, this method can address many challenging tasks of high-speed train safety inspection, in which conventional methods cannot work well. To further improve performance, a novel multishape training method is introduced. Extensive experiments demonstrate that the proposed methods perform well.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Convolutional Neural Network‐Based Classification and Decision‐Making Model for Visible Defect Identification of High‐Speed Train Images

et al. 2021

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An Online Inspection System for Critical Train Roof Components Based on the Plane Array Camera

Zhang,

Xing,

et al. 2024

Lecture Notes in Electrical Engineering

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An improved subpixel-level registration method for image-based fault diagnosis of train bodies using SURF features

Liu¹,

Huang²,

Qin³

et al. 2021

Meas. Sci. Technol.

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Using computer-vision-based fault-diagnosis technologies, anomalies in train bodies can be automatically identified as part of daily maintenance. Nevertheless, since the train speed changes constantly and nonlinearly during the process of image acquisition, the obtained images can be severely distorted. As such, it is necessary to first register the collected images to correct the distortion, so as to accurately extract the key parts of the train body for fault detection. This paper proposes a new accurate registration method for train images based on speeded-up robust features, which improves on the traditional segment registration (SR) method by adding procedures for feature selection, piecewise linearization, and interpolated registration. The novelty of the proposed method is that up to subpixel-level registration accuracy can be achieved. Meanwhile, the robustness of the algorithm is ensured by its strong ability to screen out non-matching pairs when selecting features for registration. To demonstrate the performance of the proposed scheme, experimental comparison and verification are conducted, which show that, compared with the traditional SR method, the registration accuracy is remarkably improved in terms of mean square error, normalization cross-correlation, and structural similarity index.

show abstract

Abnormal Target Detection of High-Speed Train's Roof

Cited by 5 publications

References 10 publications

A Convolutional Neural Network‐Based Classification and Decision‐Making Model for Visible Defect Identification of High‐Speed Train Images

A Convolutional Neural Network‐Based Classification and Decision‐Making Model for Visible Defect Identification of High‐Speed Train Images

An Online Inspection System for Critical Train Roof Components Based on the Plane Array Camera

An improved subpixel-level registration method for image-based fault diagnosis of train bodies using SURF features

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