Laser & Optoelectronics Progress, Volume. 58, Issue 2, 0215009(2021)
Rail Surface Damage Detection Method Based on Improved U-Net Convolutional Neural Network
The deep learning method based on convolutional neural network plays a very important role in promoting the automatic detection of rail surface damage. Therefore, a method based on convolutional neural network for rail surface damage detection is proposed. First, a branch network is added between the contraction path and extension path of the classic U-Net can assist U-Net to output the ideal segmentation graph. Then, the type-I RSDDs high-speed railway track dataset is taken as the test sample, and the test sample is amplified by means of data enhancement and fed into the improved U-Net for training and testing. Finally, the evaluation index is used to evaluate the proposed method. The experimental results show that the detection accuracy of the proposed method reaches 99.76%, which is 6.74 percentage higher than the highest level of other methods, indicating that the proposed method can significantly improve the detection accuracy.
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Bo Liang, Jun Lu, Yang Cao. Rail Surface Damage Detection Method Based on Improved U-Net Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0215009
Category: Machine Vision
Received: Jun. 5, 2020
Accepted: Aug. 3, 2020
Published Online: Jan. 11, 2021
The Author Email: Lu Jun (lujun@sust.edu.cn)