Underwater image processing method for fish localization and detection in submarine environment |
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| Affiliation: | 1. School of Electrical and Information Engineering, Tianjin University, Weijing Road 92, Tianjin and 300300, China;2. Department of Criminal Science and Technique, China Criminal Police University, Huanggudi Strict Tawan Street 83, Shenyang and 11000, China;1. School of Information Engineering, Yangzhou University, China;2. Dept. of Mechanical and Control Engineering, Kyushu Institute of Technology, Japan;3. State Key Laboratory of Marine Geology, Tongji University, China;4. State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, China;5. Dept. of Electrical and Electronic Engineering, Kyushu Institute of Technology, Japan |
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| Abstract: | Object detection is an important process in image processing, it aims to detect instances of semantic objects of a certain class in digital images and videos. Object detection has applications in many areas of computer vision such as underwater fish detection. In this paper we present a method for preprocessing and fish localization in underwater images. We are based on a Poisson–Gauss theory, because it can accurately describe the noise present in a large variety of imaging systems. In the preprocessing step we denoise and restore the raw images. These images are split into regions utilizing the mean shift algorithm. For each region, statistical estimation is done independently in order to combine regions into objects. The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms state of the art methods. |
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| Keywords: | Object detection Image denoising Scene understanding Underwater image processing |
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