Deep learning-based disease detection framework for ultra-high resolution images of tunnels

doi:10.13474/j.cnki.11-2246.2025.0318

Abstract

Abstract: The data collected by existing tunnel detection techniques usually obtains ultra-high resolution images, and the actual area of the disease in the tunnel is small, which makes the loss of disease information occur after the image has been simply pre-processed (e.g., scaled), and the deep learning model trained under limited computational resources may have a reduced detection rate of the object, unstable training, and other phenomena. To address the above problems, this paper proposes a framework for disease detection based on deep learning and ultra-high resolution images of tunnels, which is applicable to any deep learning model by pre-processing the ultra-high resolution image, segmenting the original image into smaller patch images, and resizing the ultra-high resolution image to a suitable size to improve the performance of the detection model. The experimental results show that the performance of the model under the proposed framework improve by about 77.19% compared with conventional detection process. And the proposed framework is applicable to general ultra-high resolution images, which can effectively identify the damages of general structures other than tunnels.

Key words: tunnel inspection, ultra-high resolution images, deep learning, disease detection, image pre-processing

CLC Number:

P237

MA Haizhi. Deep learning-based disease detection framework for ultra-high resolution images of tunnels[J]. Bulletin of Surveying and Mapping, 2025, 0(3): 105-110.

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