Abstract
Fast and robust detection of a small target in an infrared image is one of the key techniques in infrared search and track systems. The top-hat transformation is an important technology in the field of infrared target detection. Many algorithms have been proposed to improve the top-hat transformation by modifying different structural elements. However, these methods can cause a high false alarm rate under conditions of small dim targets, low signal-to-noise, and complex background. To overcome this limitation, considering both detection performance and speed, an effective and robust detection model using multidirectional structural elements is proposed in this article. First, the nonconcentric multidirectional ring structural elements are designed. Then, using the morphological transformation with designed structural elements, the background is suppressed, and a small target is enhanced in different directions. Finally, the results of the morphological transformations are fused to obtain the final result, further highlighting a small target. The experimental results demonstrate the robust performance of the proposed method for various backgrounds and targets. In addition, the proposed method can achieve a detection speed of up to 10.4 ms per frame under the image resolution of 240 $ \times $ 320 pixels. Therefore, the proposed method is suitable for real-time applications.
Highlights
INFRARED small target detection is one of the key techniques in many fields, including maritime rescue and surveillance [1], precision guidance [2], remote sensing [3], and infrared searching and tracking systems [4]
Using the morphological transformation with designed structural elements, the background is suppressed, and a small target is enhanced in different directions
This structure is divided into two parts, where the central part denotes the structural elements of the ring Top-Hat transformation, and the surrounding part denotes the structural elements in different directions
Summary
INFRARED small target detection is one of the key techniques in many fields, including maritime rescue and surveillance [1], precision guidance [2], remote sensing [3], and infrared searching and tracking systems [4]. The sequential-frames-based algorithms usually rely on the assumption that the information on target and background is consistent between frames, and they require prior knowledge about target shape and speed [5] This prior knowledge is difficult to obtain in practical applications [6], so the single-frame small infrared target detection algorithms have been widely used. The single-frame small infrared target detection has attracted great research attention, and many methods have been proposed, including background suppression-based methods [7], human visual system-based methods [8], sparse and low-rank matrices recovery-based methods, and deep learning-based methods. Using the morphological transformation with designed structural elements, the background is suppressed, and a small target is enhanced in different directions.
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