Tunnel surface defect segmentation method based on deep learning

Abstract

The invention discloses a tunnel surface defect segmentation method based on deep learning. The method comprises the following steps: 1, acquiring an image; 2, preprocessing the image and dividing data sets; 3, constructing a network: replacing a final full connection layer of the network with a convolution layer on the basis of a deep residual network; 4, training the deep neural network, initializing network parameters, and setting an initial learning rate; 5, inputting training and verification integration batches obtained after processing in step 2 into a deep convolutional neural network,updating the network parameters in each batch, setting training stop conditions, and stopping training when the conditions are met to obtain a final model; and 5, segmenting a tunnel surface image tobe detected by using the model trained in step 4, inputting the tunnel surface image into the deep neural network, and outputting a pixel-level defect segmentation result graph by the network. The method can quickly judge whether the tunnel surface image has defects or not, gives out defect types and positions of the defects, and has the advantages of being high in efficiency, high in accuracy and high in practicability.

Description

technical field [0001] The invention relates to a tunnel surface defect segmentation method, in particular to a tunnel surface defect segmentation method based on deep learning, which belongs to the fields of computer vision and image processing. Background technique [0002] Tunnel is an important railway facility, and its condition directly affects railway traffic safety and transportation efficiency. With the implementation of my country's railway speed-up strategy, higher requirements have been put forward for the safety of trains. At the same time, the increase in operating speed and the operation of heavy-duty trains have increased the damage to the track, leading to aggravated deterioration of the track state. Therefore, regular inspection of tunnels, early detection of damage and timely repairs to avoid accidents have become a basic task in railway work. [0003] At present, the technical methods of tunnel surface defect detection mainly include artificial visual in...

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Problems solved by technology

The visual method is labor-intensive, dangerous, time-consuming, low-efficiency, and the measurement results are subject to subjective influence; the magnetic particle method has high operating costs, and at the same time, the classification accuracy is low and the detection speed is low; the eddy current method has high-frequency excitation signal, which makes the system structure and signal processing more complex, and the detection efficiency is relatively low

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