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  • 1
    Online Resource
    Online Resource
    Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network
    Hindawi Limited ; 2022
    In:  Journal of Advanced Transportation Vol. 2022 ( 2022-1-11), p. 1-14
    Details
    In: Journal of Advanced Transportation, Hindawi Limited, Vol. 2022 ( 2022-1-11), p. 1-14
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2042-3195 , 0197-6729
    URL: Article
    DOI: 10.1155/2022/7851562
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2553327-7
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  • 2
    Online Resource
    Online Resource
    A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network
    Computers, Materials and Continua (Tech Science Press) ; 2023
    In:  Computer Modeling in Engineering & Sciences Vol. 134, No. 3 ( 2023), p. 1671-1706
    Details
    In: Computer Modeling in Engineering & Sciences, Computers, Materials and Continua (Tech Science Press), Vol. 134, No. 3 ( 2023), p. 1671-1706
    Type of Medium: Online Resource
    ISSN: 1526-1506
    URL: Article
    DOI: 10.32604/cmes.2022.022143
    Language: English
    Publisher: Computers, Materials and Continua (Tech Science Press)
    Publication Date: 2023
    detail.hit.zdb_id: 2025779-X
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  • 3
    Online Resource
    Online Resource
    Semantic-Segmentation-Based Rail Fastener State Recognition Algorithm
    Hindawi Limited ; 2021
    In:  Mathematical Problems in Engineering Vol. 2021 ( 2021-3-2), p. 1-15
    Details
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2021 ( 2021-3-2), p. 1-15
    Abstract: Rail fastener status recognition and detection are key steps in the inspection of the rail area status and function of real engineering projects. With the development of and widespread interest in image processing techniques and deep learning theory, detection methods that combine the two have yielded promising results in practical detection applications. In this paper, a semantic-segmentation-based algorithm for the state recognition of rail fasteners is proposed. On the one hand, we propose a functional area location and annotation method based on a salient detection model and construct a novel slab-fastclip-type rail fastener dataset. On the other hand, we propose a semantic-segmentation-framework-based model for rail fastener detection, where we detect and classify rail fastener states by combining the pyramid scene analysis network (PSPNet) and vector geometry measurements. Experimental results prove the validity and superiority of the proposed method, which can be introduced into practical engineering projects.
    Type of Medium: Online Resource
    ISSN: 1563-5147 , 1024-123X
    URL: Article
    DOI: 10.1155/2021/8956164
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 4
    Online Resource
    Online Resource
    Saliency Optimization and Integration Via Iterative Bootstrap Learning
    World Scientific Pub Co Pte Ltd ; 2018
    In:  International Journal of Pattern Recognition and Artificial Intelligence Vol. 32, No. 09 ( 2018-09), p. 1859016-
    Details
    In: International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Pub Co Pte Ltd, Vol. 32, No. 09 ( 2018-09), p. 1859016-
    Abstract: This paper proposes an effective method to elevate the performance of saliency detection via iterative bootstrap learning, which consists of two tasks including saliency optimization and saliency integration. Specifically, first, multiscale segmentation and feature extraction are performed on the input image successively. Second, prior saliency maps are generated using existing saliency models, which are used to generate the initial saliency map. Third, prior maps are fed into the saliency regressor together, where training samples are collected from the prior maps at multiple scales and the random forest regressor is learned from such training data. An integration of the initial saliency map and the output of saliency regressor is deployed to generate the coarse saliency map. Finally, in order to improve the quality of saliency map further, both initial and coarse saliency maps are fed into the saliency regressor together, and then the output of the saliency regressor, the initial saliency map as well as the coarse saliency map are integrated into the final saliency map. Experimental results on three public data sets demonstrate that the proposed method consistently achieves the best performance and significant improvement can be obtained when applying our method to existing saliency models.
    Type of Medium: Online Resource
    ISSN: 0218-0014 , 1793-6381
    URL: Article
    DOI: 10.1142/S0218001418590164
    Language: English
    Publisher: World Scientific Pub Co Pte Ltd
    Publication Date: 2018
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  • 5
    Online Resource
    Online Resource
    AFI-Net: Attention-Guided Feature Integration Network for RGBD Saliency Detection
    Hindawi Limited ; 2021
    In:  Computational Intelligence and Neuroscience Vol. 2021 ( 2021-3-30), p. 1-10
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2021 ( 2021-3-30), p. 1-10
    Abstract: This article proposes an innovative RGBD saliency model, that is, attention-guided feature integration network, which can extract and fuse features and perform saliency inference. Specifically, the model first extracts multimodal and level deep features. Then, a series of attention modules are deployed to the multilevel RGB and depth features, yielding enhanced deep features. Next, the enhanced multimodal deep features are hierarchically fused. Lastly, the RGB and depth boundary features, that is, low-level spatial details, are added to the integrated feature to perform saliency inference. The key points of the AFI-Net are the attention-guided feature enhancement and the boundary-aware saliency inference, where the attention module indicates salient objects coarsely, and the boundary information is used to equip the deep feature with more spatial details. Therefore, salient objects are well characterized, that is, well highlighted. The comprehensive experiments on five challenging public RGBD datasets clearly exhibit the superiority and effectiveness of the proposed AFI-Net.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2021/8861446
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2388208-6
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  • 6
    Online Resource
    Online Resource
    A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network
    Hindawi Limited ; 2021
    In:  Computational Intelligence and Neuroscience Vol. 2021 ( 2021-7-29), p. 1-15
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2021 ( 2021-7-29), p. 1-15
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2021/2565500
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2388208-6
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