Geological discontinuity (GD) plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses. Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data. Inspired by recent advances in computer vision, deep learning (DL) models have been widely utilized for image-based fracture identification. The multi-scale characteristics, image resolution and annotation quality of images will cause a scale-space effect (SSE) that makes features indistinguishable from noise, directly affecting the accuracy. However, this effect has not received adequate attention. Herein, we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques. Next, we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Combining these metrics with the scale-space theory, we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition. It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models. In light of these observations, we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation. The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD. It also facilitates the objective understanding, description and analysis of the rock behavior and stability of slopes from the perspective of image data.

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Keywords: Image processing, Geological discontinuities, Deep learning, Multi-scale, Scale-space theory, Scale hybridization