Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves wood utilization. Traditional neural network techniques have not yet been employed for wood defect detection due to long training time, low recognition accuracy, and nonautomatical extraction of defect image features. In this work, a model (so-called ReSENet-18) for wood knot defect detection that combined deep learning and transfer learning is proposed. The “squeeze-and-excitation” (SE) module is firstly embedded into the “residual basic block” structure for a “SE-Basic-Block” module construction. This model has the advantages of the features that are extracted in the channel dimension, and it is fused in multiscale with original features. Instantaneously, the fully connected layer is replaced with a global average pooling; consequently, the model parameters could be reduced effectively. The experimental results show that the accuracy has reached 99.02%, meanwhile the training time is also reduced. It shows that the proposed deep convolutional neural network based on ReSENet-18 combined with transfer learning can improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

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

Computational Intelligence and Neuroscience ◽

10.1155/2021/2565500 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Danyang Zheng ◽

Liming Li ◽

Shubin Zheng ◽

Xiaodong Chai ◽

Shuguang Zhao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Defect Detection ◽

High Speed ◽

Surface Defects ◽

Detection Method ◽

Experimental Tests ◽

Deep Convolutional Neural Network ◽

Railway Track ◽

Model Based

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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