Infrared Small-Target Detection via Improved Density Peak Clustering and Gray-Level Contribution

With the increasing deployment of multiple-warhead missiles and UAV swarms, the challenge of achieving high-performance detection of clustered multismall targets has emerged as an imperative issue within infrared (IR) air defense systems. However, the existing methods struggle to accurately characterize the features of clustered targets, resulting in poor performance for clustered target detection. To address this challenge, we propose an IR small-target detection method based on improved density peak clustering (DPC) and gray-level contribution. Specifically, we first introduce attribute filtering to quickly extract candidate targets. Note that the attribute cannot only guide the parameter setting of the improved DPC (IDPC) but also derive the weights of feature fusion. Then, we construct an unsupervised clustering model based on IDPC, which is tailored for detecting clustered targets and can accurately represent the local features of these targets. In addition, a gray-level contribution model is proposed to extract the global features of small targets, leveraging the statistical properties of the gray level of small targets. By the weighted fusion of local and global features, the clustered targets are effectively enhanced, while the background clutter is further suppressed. Extensive experimental results demonstrate that our method exhibits a superior clustered target enhancement effect and a higher probability of multitarget detection compared with the state-of-the-art methods.