Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural scene understanding. Despite decades of effort and many achievements, there are still challenges in feature extraction and model design. In this paper, we review the advancement in image segmentation methods systematically. According to the segmentation principles and image data characteristics, three important stages of image segmentation are mainly reviewed, which are classic segmentation, collaborative segmentation, and semantic segmentation based on deep learning. We elaborate on the main algorithms and key techniques in each stage, compare, and summarize the advantages and defects of different segmentation models, and discuss their applicability. Finally, we analyze the main challenges and development trends of image segmentation techniques.

Effective detection of small targets plays a pivotal role in infrared (IR) search and track applications for modern military defense or attack. However, IR small targets are very difficult to detect because of their weak brightness, small size, and lack of shape, structure, texture, and other information elements. In order to simultaneously satisfy the robustness and timeliness of target detection, inspired by density peak clustering and the human visual system, an idea combining an improved density peak global search and local contrast calculation is proposed. First, the positions of candidate targets are determined in the preprocessed image using the improved density peak global search method (IDPGSM). Second, the saliency map is obtained using the double-weights enhanced local contrast method (DWELCM) for the candidate target neighborhood. Finally, adaptive threshold segmentation is used to detect IR small targets. Through comprehensive analysis of five evaluation indicators, the experimental results on seven real sequences and three hundred IR images of different scenes that the proposed method has better detection performance compared with six baseline methods. It can quickly and accurately determine the small target position in the case of severe background clutter and noise interference. Index Terms-Double-weights enhanced local contrast method (DWELCM), improved density peaks global search (IDPGSM), infrared (IR) small target, three-layer window. I. INTRODUCTIONA N INFRARED (IR) detection system is a passive detection device that works in the IR spectrum of the atmospheric window band. It is an important part of advanced electronic warfare and integrated fire-control systems. In a complex and strong electromagnetic interference environment, an IR detection system has many advantages, such as strong anti-interference ability, good concealment, ability to work during the day and night, and high measurement accuracy. Therefore, it is widely used in weapon systems for IR early warning, reconnaissance, and precision guidance in airborne, shipborne, vehicle, and spaceborne applications [1]. In a complex battlefield environment, an IR detection system is often required to quickly locate and Manuscript

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To improve the recognition probability of airborne infrared search and track systems (IRSTS) in air-to-air operations, the point target recognition probability model is established. And using an operating range model, the detection envelope is calculated for the airborne IRSTS. Combining with different operational backgrounds and specific technical parameters, the simulation results show that: (1) an increase in visual range and target speed, along with a decrease of oblique angle, produces an increase in the recognition probability; (2) the angle of maximum operating range of each detection envelope is the optimum detection point for the airborne IRSTS; and (3) the optimum threshold noise ratio is 8-10. These conclusions provide further scope in improving the operational effectiveness of airborne IRSTS in practical applications.

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