An automatic active contour method for sea cucumber segmentation in natural underwater environments

文献类型： 外文期刊

第一作者： Qiao, Xi

作者： Qiao, Xi;Bao, Jianhua;Zeng, Lihua;Li, Daoliang;Zeng, Lihua;Zou, Jian;Qiao, Xi;Bao, Jianhua;Li, Daoliang

作者机构：

关键词： Underwater image; Adaptive active contour; Color space fusion

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2017 年 135 卷

页码：

收录情况： SCI

摘要： Sea cucumbers have become an important sector of the marine industry in northern China, with a culture area exceeding one million acres and a production value over one hundred and twenty million dollars. However, sea cucumber culture and fishing are mainly dependent on manual work. To promote the development of sea cucumber culture automation, it is necessary to research sea cucumber automatic segmentation based on machine vision in natural underwater environments. Sea cucumbers usually live in an environment where lighting, visibility and stability are generally not controllable, which cause underwater images of sea cucumbers to be distorted, blurred, and severely attenuated. Moreover, sea cucumbers are flexible and colored much like sandy sediments. Therefore, it is difficult to fully separate a cucumber from the background in an underwater image. For fast and accurate automatic segmenting of sea cucumbers, an improved method based on active contour is presented in this paper. Image fusion based on the RGB color space and the contrast limited adaptive histogram equalization (CLAHE) method are used to increase the contrast of the sea cucumber thorns and body, respectively. Then, an edge detection algorithm is proposed to extract the edge of the sea cucumber thorns as an initial contour for the thorn segmentation, and a rectangular contour based on the edge information is built as the initial contour for the body segmentation. Finally, the results of the thorn and body are fused. All the procedures are automatically completed without human intervention. Qualitative and quantitative analysis indicates that the proposed method outperformed the other two compared methods in sea cucumber segmentation. A test with 120 samples showed that for the proposed method, the mean values of Euclidean distance, sensitivity, specificity, and accuracy were 12.7, 84.51, 96.97, and 96.54, respectively. The average time to run the algorithm for all images is 4.27 s. Thus, the proposed method could work for sea cucumber monitoring and fishing in real time. (C) 2017 Published by Elsevier B.V.

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