A Tunnel Crack Segmentation and Recognition Algorithm Using SPGD-and-Generative Adversarial Network Fusion

TL;DR

This paper introduces a new algorithm that improves the detection of tunnel cracks using a combination of SPGD and a generative adversarial network.

Contribution

The novel contribution is a fusion of SPGD and an improved GAN for enhanced tunnel crack segmentation and recognition.

Findings

The SPGD algorithm enhances tunnel crack image clarity and edge details.

The improved GAN effectively segments small tunnel cracks after SPGD processing.

The proposed algorithm outperforms other methods in tunnel crack recognition rate.

Abstract

In order to improve the recognition ability of tunnel cracks in the UAV platform with a vision imaging system in the UAV platform with a vision imaging system, this paper proposes a tunnel crack segmentation algorithm using SPGD-and-generative adversarial network fusion. The SPGD algorithm can enhance the detail and edge information of a tunnel crack image, which improves the clarity of the tunnel crack image. The new generative adversarial network (GAN) is designed by using an improved U-Net generator and full convolutional network (FCN) discriminator to form a new network; the improved generative adversarial network can effectively segment tunnel crack images after stochastic parallel gradient descent (SPGD) algorithm processing, especially the texture feature extraction and segmentation of small tunnel cracks, which can improve the rate of recognition of tunnel cracks. Based on collected tunnel crack image data, we selected 12 typical tunnel crack images and verified the rationality and advanced nature of the proposed recognition algorithm by comparing it with other recognition methods. The results show that the recognition rate of the proposed tunnel crack recognition algorithm was significantly improved.

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