Sea surface image semantic segmentation method based on space constraint hybrid model capable of being simplified

Abstract

The invention discloses a sea surface image semantic segmentation method based on a space constraint hybrid model capable of being simplified. The sea surface image semantic segmentation method comprises the following steps: (1) inputting a sea surface color image to be detected; (2) supposing that the sea surface image has three main semantic regions including sky, coast / haze and seawater and a potential obstacle region, and establishing a spatial constraint hybrid model according to the three main semantic regions; (3) optimizing the spatial constraint hybrid model by using an expectation maximization algorithm (EM); (4) calculating a KL distance (Kullback-Leibler divergence) of Gaussian distribution of sky, coast / haze categories, and if the KL distance is smaller than a set threshold value, simplifying the spatial constraint hybrid model; and (5) outputting a sea surface image semantic segmentation result. According to the method, semantic segmentation can be effectively carried outon the sea surface image, and the method has the advantages of being high in speed and good in robustness.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a sea surface image semantic segmentation method based on a simplified space-constrained mixed model. Background technique [0002] Image semantic segmentation is a basic technology for computer vision understanding. Its task is to segment different objects in the image from the perspective of pixels and semantically label each pixel (that is, classify). Applying semantic segmentation technology to sea surface images can help enhance the perception of the surrounding environment of unmanned surface vehicles, thereby ensuring their safe operations. [0003] In recent years, with the rapid development of deep learning, the semantic segmentation method based on convolutional neural network has been widely researched and applied in the fields of unmanned driving and medical image analysis. However, there are few related studies on semantic segmentation of sea surface images...

AI Technical Summary

Problems solved by technology

This method has better detection performance and faster speed; however, by observing the sea surface images taken by the unmanned surface vehicle, it can be found that when the unmanned surface vehicle is driving away from the coast, the sea surface image usually only has the sky area, sea water area and potential obstacles area, there is no middle coast / haze mixed area assumed by Kristan et al. Therefore, the semantic segmentation results of the method proposed by Kristan et al.

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