The Identification and Assessment of Rail Corrugation Based on Computer Vision

Wei, Dehua; Wei, Xiukun; Liu, Yuxin; Jia, Limin

doi:10.3390/app9183913

Cited by 13 publications

(6 citation statements)

References 34 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The OpenCV library, such as 'cvCalibateCamera', 'cvtColor', 'Threshold', and 'Corner Detection', is used for camera calibration, referring to the chessboard corner point, and the image pixels of a chessboard target are tracked. (10)(11)(12)(13) Computer vision technology has been applied to the detection of construction deformation and movement using data from site images and videos as an alternative to time-consuming and manual traditional practices. (14)(15)(16)(17)(18) Thus, the relative displacements between the camera and the chessboard in the x-, y-, and z-directions can be measured using computer vision technology.…”

Section: Introductionmentioning

confidence: 99%

Optical Crackmeter for Retaining Wall in a Landslide Area Using Computer Vision Technology

Chen¹,

Chen²,

Chen³

et al. 2021

Sensors and Materials

View full text Add to dashboard Cite

An innovative 3D optical crackmeter employing computer vision technology is used for displacement monitoring in a crack of a retaining wall automatically and remotely. The 3D optical crackmeter is composed of a Raspberry Pi device and a digital camera in a box, and a fixed chessboard on the two sides of a crack. A network with LoRa wireless communication can be connected as an IoT system to provide automatic remote functions. The OpenCV library is employed to analyze changes in chessboard imaging so that relative displacements of the crack in the retaining wall can be measured in a landslide area. Through laboratory and field testing, the resolution and accuracy of the 3D optical crackmeter were determined as 0.04 and 0.1 mm, respectively. Using the crackmeter, we observed significant displacements in the xand z-directions of the crack in a retaining wall of 0.067 and 0.060 cm, respectively, in the Jhongsinlun landslide area of Taiwan over three months. Overall, the 3D optical crackmeter with computer vision technology can accurately measure the 3D displacement of cracks in a retaining wall. Moreover, the IoT-based 3D optical crackmeter is more cost-effective than traditional crackmeters used in landslide areas.

show abstract

Section: Introductionmentioning

confidence: 99%

Optical Crackmeter for Retaining Wall in a Landslide Area Using Computer Vision Technology

Chen¹,

Chen²,

Chen³

et al. 2021

Sensors and Materials

View full text Add to dashboard Cite

show abstract

“…Currently there is no primary cure for rail corrugation and rail grinding remains the common treatment [8][9]. Researchers in the recent years are developing methods to detect, identify and study rail corrugation through computer vision [10][11][12]. People who live closer to affected railway sites are exposed to mechanical vibrations that may negatively impact on their well-being.…”

Section: Introductionmentioning

confidence: 99%

Train Wheels Eigenmodes on Effects of Vibration and Track Deterioration in the Belfast to Steelpoort Railway Line

Balekwa

Kallon

2021

MATEC Web Conf.

View full text Add to dashboard Cite

In South Africa, one of the main lines situated in its Limpopo Province presents symptoms of excessive vehicle-track vibrations. A modal method is adopted to study the difference in dynamic response of corrugated rails resting on the Hytrel/6385 and High Density Polyethylene (HDPE) rail pads. The results for dynamic response of rails on these two types of rail pads are analysed in form of Frequency Response Functions (FRFs). Two positions are considered in obtaining these point FRFs; those are mid-span and on top of sleeper. Resonance modes of significance are excited in a frequency range of 0 – 500Hz on rails resting on both types of rail pads. In view of the entire vibration response window, the FRFs show a more damped vibration response by rail resting on the HDPE than that resting on the Hytrel/6358 rail pad. A Finite Element (FE) modal analysis method is used to investigate vibration mode shapes and corresponding frequencies of the wheelset used by the locomotive operating in the line. In the high frequency range of 350 – 500Hz, the gear-side wheel of a locomotive wheelset proves to be vibrating more erratically than the non-gear side wheel. Clearly pronounced mode shape excited at a frequency of 350Hz and higher are presented.

show abstract

“…For the detection of rail corrugation, there are methods based on spatial features and frequency domain features. Spatial feature-based methods [18]- [20] mainly use spatial feature extraction followed by classification algorithms to identify rail corrugation, in which a high recognition precision is obtained. But, the method is sensitive to the setting of algorithm parameters.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Defect Identification for Railway Track Line Based on Image Processing and Improved YOLOv3 Model

et al. 2020

Self Cite

View full text Add to dashboard Cite

The condition monitoring of railway track line is one of the essential tasks to ensure the safety of the railway transportation system. Railway track line is mainly composed of tracks, fasteners, sleepers, and so on. Given the requirements for rapid and accurate inspection, innovative and intelligent methods for multi-target defect identification of the railway track line using image processing and deep learning methods are proposed in this paper. Firstly, the track and fastener positioning method based on variance projection and wavelet transform is introduced. After that, a bag-of-visual-word (BOVW) model combined with spatial pyramid decomposition is proposed for railway track line multi-target defect detection with a detection accuracy of 96.26%. Secondly, an improved YOLOv3 model named TLMDDNet (Track Line Multi-target Defect Detection Network), integrating scale reduction and feature concatenation, is proposed to enhance detection accuracy and efficiency. Finally, to reduce model complexity and further improve the detection speed, with the help of dense connection structure, a lightweight design strategy for the TLMDDNet model named DC-TLMDDNet (Dense Connection Based TLMDDNet) is proposed, in which the DenseNet is applied to optimize feature extraction layers in the backbone network of TLMDDNet. The effectiveness of the proposed methods is demonstrated by the experimental results. INDEX TERMS Railway track line defects, multi-target defect identification, image processing, deep learning, YOLOv3.

show abstract

The Identification and Assessment of Rail Corrugation Based on Computer Vision

Cited by 13 publications

References 34 publications

Optical Crackmeter for Retaining Wall in a Landslide Area Using Computer Vision Technology

Optical Crackmeter for Retaining Wall in a Landslide Area Using Computer Vision Technology

Train Wheels Eigenmodes on Effects of Vibration and Track Deterioration in the Belfast to Steelpoort Railway Line

Multi-Target Defect Identification for Railway Track Line Based on Image Processing and Improved YOLOv3 Model

Contact Info

Product

Resources

About