Volume 18, Issue 3 (12-2021)                   JSDP 2021, 18(3): 147-160 | Back to browse issues page

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Mahmoodzadeh A. Marine Target Detection in Noisy Infrared Images using a Hybrid Recognition Algorithm. JSDP. 2021; 18 (3) :147-160
URL: http://jsdp.rcisp.ac.ir/article-1-1061-en.html
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University
Abstract:   (356 Views)
Maritime transportation system is a vital part of the world conveyance. The surveillance in maritime industry and detecting marine targets have a great impact on military and commercial applications. Daily increase in maritime zone encourages the researchers to develop intelligent surveillance approaches in the maritime transportation. The sensing methods generally include visual and infrared cameras, sensors, and radars. Cameras are widely used since they capture high resolution images than sensors and traditional radars. Also, applying complex pattern recognition techniques and decision-making processes to the camera images provides more accurate detection results. Due to the clutters, dust, and weather changes in the sea including the rainfall, snowfall, and heavy fog, the image quality taken by the visual cameras is drastically deteriorated. Also, detecting the sea targets -specially the small ones- and similarly the sea-sky horizon line becomes more challenging. In such situations, the infrared images reveal higher performance and accuracy in comparison with visible images. The sea-sky horizon line detection of noisy infrared images in small target detection algorithms with high intensity and low SNR is of great importance in maritime surveillance. Determining the horizon line simplifies the target detection by restricting the search area for the targets in the image. This task decreases the computation time and mistakes in the detection.
This paper presents a method for detecting marine targets in noisy infrared images. The proposed method includes two steps of detecting the sea-sky horizon line and finding the targets. In the first step, the two-dimensional gradient of the image is computed, from which it is observed that the most variations are appeared at the edge points. With respect to this remark, the maximum of each column of the gradient image is found and the obtained values for all columns and corresponding rows’ numbers are kept in a set, namely the maximum pixels set. Then, to find the sea-sky horizon line, on the first and the last 75 pixels in the mentioned set, a straight line is fitted along the image width. Afterwards, to search for the objects, a region of interest is selected around the detected line. Restricting the search region increases the speed of the proposed method and decreases the number of false alarms. In the second step, this region is partitioned into some separate blocks; from each, multiple features are extracted. These features are fed into multiple classifiers whose outputs are given to a decision-making algorithm based on the interval type-II fuzzy fusion system. This system decides to which class (target or background) that block belongs. Finally, the objects are found by integrating the target blocks and removing the unwanted ones.
To evaluate the proposed method, first an image dataset was generated using an infrared camera with medium wavelength in different situations. This was done due to no access to a complete infrared sea image bank. Sea infrared images were commonly corrupted by a combination of noises including the salt-and-pepper, Gaussian noise or electronic noises due to the detector of camera image supply. In order to attenuate these noises, a 3×3 median filter was applied to the raw image. Afterwards, to increase the image contrast, the histogram equalization method was performed. Finally, the proposed approach was run to find the marine targets in the enhanced image. The results demonstrated that the sea-sky horizon line was detected with low computational complexity and high accuracy while targets were also found with desirable detection rates.
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Type of Study: Research | Subject: Paper
Received: 2019/08/18 | Accepted: 2021/05/10 | Published: 2022/01/20 | ePublished: 2022/01/20

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