Improved DBSCAN for Infrared Cluster Small Target Detection

With the development of modern weapons such as UAV swarms and multi-warhead missiles, infrared (IR) cluster small target detection technology has become increasingly important. However, the difficulty in characterizing cluster multi-targets leads to poor detection performance of existing methods. On the one hand, this paper proposes improved DBSCAN (IDBSCAN) to accurately extract the features of cluster multi-targets with unknown number and distribution. On the other hand, IDBSCAN-based difference measure (IDBSCAN-DM) is proposed, which fuses saliency and distribution features to further enhance cluster multi-targets. Specifically, we first design the multiscale sliding window to quickly extract candidate targets. Then, the IDBSCAN-based local window is constructed and IDBSCAN-DM is computed for better target enhancement and background suppression. Finally, adaptive threshold segmentation is performed on the IDBSCAN-DM map to detect real targets. Extensive comparative experiments demonstrate that the proposed method achieves better target enhancement and higher probability of detection.