文章基本信息

标题：Semantic Segmentation of Underwater Images Based on Improved Deeplab
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作者：Fangfang Liu ; Ming Fang
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2020
卷号：8
期号：3
页码：188
DOI：10.3390/jmse8030188
语种：English
出版社：MDPI AG
摘要：Image semantic segmentation technology has been increasingly applied in many fields, for example, autonomous driving, indoor navigation, virtual reality and augmented reality. However, underwater scenes, where there is a huge amount of marine biological resources and irreplaceable biological gene banks that need to be researched and exploited, are limited. In this paper, image semantic segmentation technology is exploited to study underwater scenes. We extend the current state-of-the-art semantic segmentation network DeepLabv3 + and employ it as the basic framework. First, the unsupervised color correction method (UCM) module is introduced to the encoder structure of the framework to improve the quality of the image. Moreover, two up-sampling layers are added to the decoder structure to retain more target features and object boundary information. The model is trained by fine-tuning and optimizing relevant parameters. Experimental results indicate that the image obtained by our method demonstrates better performance in improving the appearance of the segmented target object and avoiding its pixels from mingling with other class’s pixels, enhancing the segmentation accuracy of the target boundaries and retaining more feature information. Compared with the original method, our method improves the segmentation accuracy by 3%.