Deformable convolution fusion enhanced streetscape image semantic segmentation method

Abstract

The invention discloses a deformable convolution fusion enhanced streetscape image semantic segmentation method, which comprises a training stage and a test stage, and comprises the following steps of: constructing a streetscape image semantic segmentation deep neural network model to ensure that the network model obtains more small target feature information while a streetscape image large targetobject is segmented; therefore, the problems of small-scale target loss and discontinuous segmentation during streetscape image semantic segmentation are solved, the image segmentation effect is improved, the overall robustness of the model is better, and the streetscape image processing precision is higher.

Description

technical field [0001] The invention belongs to the technical field of computer vision, relates to image processing technology, and in particular to a semantic segmentation method of street view images enhanced by deformable convolution fusion. Background technique [0002] Image semantic segmentation is an important branch of computer vision in the field of artificial intelligence, and an important part of image understanding and analysis in machine vision. Image semantic segmentation is to accurately classify each pixel in the image to its category, making it consistent with the visual representation of the image itself, so the image semantic segmentation task is also called pixel-level image classification task. At present, semantic segmentation has been widely used in scenarios such as automatic driving and drone landing point determination. [0003] Convolutional neural networks have achieved success in image classification, localization, and scene understanding. With...

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Problems solved by technology

[0005] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method for semantic segmentation of street view images that is enhanced by deformable convolution fusion, and constructs a deep neural network model for semantic segmentation of street view images, so that the network model can be used when large objects in street view images are segmented. At the same time, more small target feature information can be obtained, thus solving the problem of small-scale target loss and segmentation discontinuity in the semantic segmentation of street view images. The overall robustness of the model is better, the processing accuracy of street view images is higher, and image segmentation is improved. Effect

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