A Comprehensive Detection System for Track Geometry Using Fused Vision and Inertia

Peng, Lele; Zheng, Shibao; Li, Peixing; Wang, Yuling; Zhong, Qianwen

doi:10.1109/tim.2020.3039301

Cited by 26 publications

(15 citation statements)

References 61 publications

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“…Following the picture penalization procedure, the picture concentrated effort procedure is used to remove noisy pixels and the noncrack objects mentioned in Figure 2. The research work is considered an image-based automatic crack identification model for postdisaster building evolution; the authors show that the suggested method can provide significant benefits in postdisaster building element analysis based on a numerical experiment [26,27]. Furthermore, one of the most important tasks of concrete surveys is to detect cracks that occur on the pavement surface.…”

Section: Existing Methods and Review Of Literaturementioning

confidence: 99%

IoT‐Based Intelligent System for Internal Crack Detection in Building Blocks

Babu¹,

Kumar

Jayagopal

et al. 2022

Journal of Nanomaterials

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Cracks that are detected in concrete structures represent significant damage, and they can lead to a detrimental effect on the structure’s durability. Their identification in a timely manner can help ensure structural safety and guide in-depth maintenance operation. Automatic detection of such cracks has been proposed using internal crack detection utilizing ultrasonic sensors in concrete. Cracks within the concrete can be detected using ultrasonic sensors. In this investigation, we introduced an intelligent method that is aimed at developing a crack detection scheme using ultrasonic sensors. These ultrasonic sensors are used for the detection of cracks in buildings which cannot be seen with our naked eyes; they are capable of alerting authorities via SMS message and providing the cracks’ location via GSM and GPS modules. To monitor internal cracks in the concrete cubes and cylinders, the ultrasonic sensors can be fixed at the centre of the cube which will be used for interval crack monitoring based on crack detection technology. The grade of concrete used for testing is M25, and it is well mixed with the ingredients of cement, fine aggregate, coarse aggregate, and water. The concrete is placed in the cube moulds having the dimensions 150 mm × 150 mm × 150 mm . The cylinders used in the case of the experimental analysis are of the dimensions of 150 mm diameter and 300 mm height. These specimens are cast and kept in the curing tank for 28 days to attain the maximum strength. After completion of the curing period, the specimens were taken out from the tank and weighed. After this weighing process, the cubes and cylinders are about 8.884 kg and 13.399 kg, respectively. The information about the cracks can be displayed on the LCD, and also, the transmitted short message about the cracks can be exchanged between the devices using IoT.

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Section: Existing Methods and Review Of Literaturementioning

confidence: 99%

IoT‐Based Intelligent System for Internal Crack Detection in Building Blocks

Babu¹,

Kumar

Jayagopal

et al. 2022

Journal of Nanomaterials

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“…is situation poses a safety hazard to trains passing at a high speed. In recent years, nondestructive testing techniques, such as those in the literature [2], have been widely used in the maintenance of track facilities. is method of sleeper cracking can be quick and efficient in preventing accidents.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

Zheng

Wang

et al. 2022

Journal of Advanced Transportation

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This work presents a new method for sleeper crack identification based on cascade convolutional neural network (CNN) to address the problem of low efficiency and poor accuracy in the traditional detection method of sleeper crack identification. The proposed algorithm mainly includes improved You Only Look Once version 3 (YOLOv3) and the crack recognition network, where the crack recognition network includes two modules, the crack encoder-decoder network (CEDNet) and the crack residual refinement network (CRRNet). The improved YOLOv3 network is used to identify and locate cracks on sleepers and segment them after the sleeper on the ballast bed is extracted by using the gray projection method. The sleeper is inputted into CEDNet for crack feature extraction to predict the coarse crack saliency map. The prediction graph is inputted into CRRNet to improve its edge information and local region to achieve optimization. The accuracy of the crack identification model is improved by using a mixed loss function of binary cross-entropy (BCE), structural similarity index measure (SSIM), and intersection over union (IOU). Results show that this method can accurately detect the sleeper crack image. During object detection, the proposed method is compared with YOLOv3 in terms of directly locating sleeper cracks. It has an accuracy of 96.3%, a recall rate of 91.2%, a mean average precision (mAP) of 91.5%, and frames per second (FPS) of 76.6/s. In the crack extraction part, the F-weighted is 0.831, mean absolute error (MAE) is 0.0157, and area under the curve (AUC) is 0.9453. The proposed method has better recognition, higher efficiency, and robustness compared with the other network models.

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“…Although this inspection method has the advantages of simplicity and low cost, it also has disadvantages such as low detection efficiency, high missed detection rate, and poor real-time performance. In recent years, defect detection technology based on computer vision has been widely used in industry [2][3][4][5][6]. Some scholars have begun to employ computer vision technology to detect the defects of rails and their fasteners, so that the problems of manual inspection can be solved.…”

Section: Introductionmentioning

confidence: 99%

A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network

Zheng

et al. 2021

Computational Intelligence and Neuroscience

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As a result of long-term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R-CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang-Taiyuan high-speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.

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A Comprehensive Detection System for Track Geometry Using Fused Vision and Inertia

Cited by 26 publications

References 61 publications

IoT‐Based Intelligent System for Internal Crack Detection in Building Blocks

IoT‐Based Intelligent System for Internal Crack Detection in Building Blocks

Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network

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