Infrared small target detection based on multi-perception of target features

The rapid and efficient detection of infrared dim and small targets in complex backgrounds is a critical area of research in infrared image processing. When confronted with limited target features, it becomes vital to mine and construct additional features in order to accurately differentiate the target from the surrounding background and noise. This paper presents a novel small infrared target detection method based on multi-perception of target features which involves four main stages. First, two new target features are constructed to reduce the similarity between the target and noise and smooth the background. Second, two designed filters are applied to enhance the significance of the target and achieve preliminary detection. Third, structure tensor analysis is used to remove the significant background edge regions and perceive targets with strong corner characteristics. Finally, pseudo targets are eliminated by utilizing target candidate areas to achieve the final detection. Experimental results demonstrate that this method is faster and more robust compared to existing detection algorithms, with superior adaptability and detection performance.