Underwater Sonar Image Segmentation Based on Snake Model

The underwater sonar image segmentation has been a topic of research for decades. Underwater sonar image is based on the interaction by the echo signal of sound toward the underwater objects or targets. Because of the serious noises polution and the dim target edge, the contrast and resolution of sonar images are obtaind in a decreased quanlity. This paper proposes an improved snake model that focuses on solving underwater target detection and recognition. According to the traditional snake model, it is defined as an energy minimizing spline which is influenced by external constraint forces, and it can guide the image forces to pull toward features, such as lines or edges. Compared with the traditional snake model, this snake model greedy algorithm can converge to the contours more quickly and more stably, especially in complex underwater environments. Examination of the results shows that using snake model greedy algorithm has a more clear shape accuracy.

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Sonar image recognition of underwater target based on convolutional neural network

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20213920285 ◽

2021 ◽

Vol 39 (2) ◽

pp. 285-291

Author(s):

Leilei Jin ◽

Hong LIANG ◽

Changsheng Yang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Recognition ◽

Target Recognition ◽

Saliency Detection ◽

Image Features ◽

Feature Maps ◽

Sonar Image ◽

Sonar Images ◽

Underwater Target

Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.

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Robust A*-Search Image Segmentation Algorithm for Mine-like Objects Segmentation in SONAR Images

International journal of electrical and computer engineering systems ◽

10.32985/ijeces.11.2.1 ◽

2020 ◽

Vol 11 (2) ◽

pp. 53-66

Author(s):

Ivan Aleksi ◽

Tomislav Matić ◽

Benjamin Lehmann ◽

Dieter Kraus

Keyword(s):

Image Segmentation ◽

Success Rate ◽

Mobile Robotics ◽

Ground Truth ◽

Ground Truth Data ◽

Side Scan Sonar ◽

Search Image ◽

Sonar Image ◽

Sonar Images ◽

The Cost

This paper addresses a sonar image segmentation method employing a Robust A*-Search Image Segmentation (RASIS) algorithm. RASIS is applied on Mine-Like Objects (MLO) in sonar images, where an object is defined by highlight and shadow regions, i.e. regions of high and low pixel intensities in a side-scan sonar image. RASIS uses a modified A*-Search method, which is usually used in mobile robotics for finding the shortest path where the environment map is predefined, and the start/goal locations are known. RASIS algorithm represents the image segmentation problem as a path-finding problem. Main modification concerning the original A*-Search is in the cost function that takes pixel intensities and contour curvature in order to navigate the 2D segmentation contour. The proposed method is implemented in Matlab and tested on real MLO images. MLO image dataset consist of 70 MLO images with manta mine present, and 70 MLO images with cylinder mine present. Segmentation success rate is obtained by comparing the ground truth data given by the human technician who is detecting MLOs. Measured overall success rate (highlight and shadow regions) is 91% for manta mines and 81% for cylinder mines.

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