The infrared small target detection technology has a wide range of applications in maritime defense warning and maritime border reconnaissance, especially in the maritime and sky scenes for detecting potential terrorist attacks and monitoring maritime borders. However, due to the weak nature of infrared targets and the presence of background interferences such as wave reflections and islands in maritime scenes, targets are easily submerged in the background, making small infrared targets hard to detect. We propose the multidimensional information fusion network(MIFNet) that can learn more information from limited data and achieve more accurate target segmentation. The multidimensional information fusion module calculates semantic information through the attention mechanism and fuses it with detailed information and edge information, enabling the network to achieve more accurate target position detection and avoid detecting one target as multiple ones, especially in high-precision scenes such as maritime target detection, thus effectively improving the accuracy and reliability of detection. Moreover, experiments on our constructed dataset for small infrared targets in maritime scenes demonstrate that our algorithm has advantages over other state-of-the-art algorithms, with an IoU of 79.09%, nIoU of 79.43%, F1 score of 87.88%, and AuC of 95.96%.

UAV moving object detection focuses on identifying moving objects in images captured by UAVs, with broad applications in regional surveillance and event reconnaissance. Compared to general moving object detection scenarios, UAV videos exhibit unique characteristics, including foreground sparsity and varying target scales. The direct application of conventional background modeling or motion segmentation methods from general settings may yield suboptimal performance in UAV contexts. This paper introduces an unsupervised UAV moving object detection network. Domain-specific knowledge, including spatiotemporal consistency and foreground sparsity, is integrated into the loss function to mitigate false positives caused by motion parallax and platform movement. Multi-scale features are fully utilized to address the variability in target sizes. Furthermore, we have collected a UAV moving object detection dataset from various typical scenarios, providing a benchmark for this task. Extensive experiments conducted on both our dataset and existing benchmarks demonstrate the superiority of the proposed algorithm.