Abstract
Diver target automatic detection is indispensable for underwater defense systems, such as the unmanned harbor surveillance system. It is a very challenging task due to various poses and intensity features of diver target. In addition, the background noise in sonar images is complex, which also makes the task more difficult. In this paper, we propose a diver detection method based on saliency detection for sonar images. On the basis of studying the characteristics of diver sonar images, we first decompose the original sonar image and perform median filtering on it, which can significantly improve the quality of the sonar image saliency map. We employ saliency detection technique based on frequency analysis to segment the acoustic highlight region from its surroundings. This segmentation region roughly locates the diver target and generates a region of interest (ROI). We then extract the acoustic shadow region in ROI, which contributes to furtherly improve the localization accuracy. Finally, we merge the segmented highlight region and the extracted acoustic shadow region and compute the minimum outer rectangle of the merged region. Experimental results validate that the proposed method can well detect and locate the diver target, and it can also satisfy the demands of real-time application, and there is almost no false alarm in this method.
Highlights
With the characteristics of small size, high maneuverability, and difficult to detect, diver attack is a common means used by terrorist organization to attack navy vessels base, offshore platform, civil harbor, and so on [1, 2]. erefore, many countries and organizations have made great efforts for diver detection
To evaluate the performance and robustness of the proposed method, we conduct experiments of diver detection with various postures and motion directions in sonar images. e images used for the experiments are collected by imaging sonar in real underwater environment, which can be divided into four categories. e reference, sample size, and image description are shown in Table 1. e experiments are implemented on an Intel Core i5-4210U, 1.7 GHz laptop
To evaluate the detection and localization accuracy of the proposed method, we compare our approach with some classical moving object detection methods, such as background difference based on Kalman filtering (BDKF) and frame difference (FD)
Summary
With the characteristics of small size, high maneuverability, and difficult to detect, diver attack is a common means used by terrorist organization to attack navy vessels base, offshore platform, civil harbor, and so on [1, 2]. erefore, many countries and organizations have made great efforts for diver detection. E intensity level of the acoustic shadow region is close to that of dark background, and the intensity level of the highlight region is close to that of bright background. One sonar image with a diver object is usually composed of acoustic shadow region, acoustic highlight region, and background region. For such reason, it is very difficult to segment the target by using traditional threshold methods. It is notable that the shape of the acoustic shadow region is similar to that of the target and always located above the highlight region. Based on the psychological studies, human perception system is more sensitive to the salient objects. e saliency detection technique has been widely used in image segmentation, object recognition, and image retrieval [17,18,19]
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