Aiming at the shortcomings of the current Canny edge detection method in terms of noise removal, threshold setting, and edge recognition, this paper proposes a method for improving Canny edge detection by geo‐information mapping. The shortcomings of the traditional Canny edge detection method are analyzed by using the Canny optimal criterion and Tobler's First Law, which points out the direction of edge detection optimization by using the difference between edge properties and noise properties. The property characteristics and spatial distribution rules of edge points and edge lines are inspected using the geographic information mapping theory and technical methods, and edge identification criteria are defined at two levels of edge points and edge lines. Finally, the method model of improved Canny edge detection is constructed by combining guided filtering. The experimental results show that the improved edge detection method has the advantages of enriched edge details, accurate edge recognition, and strong self‐adaptive capability. This is a new attempt of geo‐information mapping theory and technical method in image edge detection, which has certain theoretical significance and strong practical guidance.
In the construction of large-scale water conservancy and hydropower transportation projects, the rock mass structural information is often used to evaluate and analyze various engineering geological problems such as high and steep slope stability, dam abutment stability, and natural rock landslide geological disasters. The complex shape and extremely irregular distribution of the structural planes make it challenging to identify and extract automatically. This study proposes a method for extracting structural planes from UAV images based on Geo-AINet ensemble learning. The UAV images of the slope are first used to generate a dense point cloud through a pipeline of SfM and PMVS; then, the multiple geological semantics, including color and texture from the image and local geological occurrence and surface roughness from the dense point cloud, are integrated with Geo-AINet for ensemble learning to obtain a set of semantic blocks; finally, the accurate extraction of structural planes is achieved through a multi-semantic hierarchical clustering strategy. Experimental results show that the structural planes extracted by the proposed method perform better integrity and edge adherence than that extracted by the AINet algorithm. In comparison with the results from the laser point cloud, the geological occurrence differences are less than three degrees, which proves the reliability of the results. This study widens the scope for surveying and mapping using remote sensing in engineering geological applications.
Audio denoising is a task to improve the perceptual quality of noisy audio signals. There is still residual noise after the denoising of noisy signals, which will affect the quality of audio data. Traditional and deep learning-based methods are still limited to the manual addition of artificial noise or low-frequency noise. Recently, audio denoising has been transformed into an image segmentation problem, and deep neural networks have been applied to solve this problem. However, its performance is limited to shallow image segmentation models. This paper proposes a novel vision transformer model for visual bird sound denoising, combining a pyramid transformer and DeepLabV3+ network (named PtDeepLab) to filter out the noise. The proposed PtDeepLab model is based on the pyramid transformer, which generates long-range and multiscale representations. The PtDeepLab model can achieve intuitive noise reduction in audio, which helps to separate clean audio from the mixture signal. Extensive experimental results showed that the proposed model has a better denoising performance than state-of-the-art methods.
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