Existing infrared small target detection algorithms often lack adaptability in complex scenes and heavily rely on parameter configurations. To address this limitation, we propose a novel infrared small target detection method based on adaptive region growing algorithm with iterative threshold analysis that leverages the homogenous compactness of the small target and discontinuity with its surroundings. Initially, the image undergoes adaptive splitting into multiple regions using an automatic seeded region growing (ASRG) algorithm, eliminating the need for preassigned seed points. Next, the segmentation results at each threshold are utilized to calculate the relative residual map (RRM) and local dissimilarity map (LDM), contributing to the selection of the optimal threshold. Finally, RRM and LDM corresponding to the optimal threshold are integrated to accurately characterize the small target signal while effectively removing background clutter. Experimental results show that the proposed method is effective in clutter removal and small target detection in diverse complex scenes, and is robust to the shape and size of targets.

Accurately and robustly detecting infrared (IR) small targets plays an essential role in infrared search and tracking (IRST) systems against complex backgrounds. However, the low signal-to-clutter ratio (SCR) feature target, also known as the dim target, is more similar to the local background region, making it prone to being misdetected as background or buried in clutter interferences, ultimately leading to detection failure. This has become a common and unavoidable challenge in the field. To alleviate this issue, an interpretation weighted sparse (IWS) method is proposed to detect IR small and dim targets while maintaining clutter suppression capability. The interpretability operation of the proposed method stems from an analysis of IR target characteristics, including dim target feature extraction and clutter suppression. Based on the multi-subspaces model, the relevant feature of the dim target is mined by a designed structure tensor, improving the target’s sparsity. Then, the contrast characteristic is introduced to eliminate the cost of dim target mine operation, namely residual clutter interference. Finally, the IWS map is obtained by fusing the target detection module and clutter suppression module, after which an adaptive threshold segmentation operation is adopted to extract the small targets. A series of experimental results and application discussion cases demonstrate that IWS outperforms other existing methods regarding detection performance while also exhibiting background suppression ability.

The long-distance ship target turns into a small spot in an infrared image, which has the characteristics of small size, weak intensity, limited texture information, and is easily affected by noise. Moreover, the presence of heavy sea clutter, including sun glints that exhibit local contrast similar to small targets, negatively impacts the performance of small-target detection methods. To address these challenges, we propose an effective detection scheme called fusion gray gradient clutter suppression (FGGCS), which leverages the disparities in grayscale and gradient between the target and its surrounding background. Firstly, we designed a harmonic contrast map (HCM) by using the two-dimensional difference of Gaussian (2D-DoG) filter and eigenvalue harmonic mean of the structure tensor to highlight high-contrast regions of interest. Secondly, a local gradient difference measure (LGDM) is designed to distinguish isotropic small targets from background edges with local gradients in a specific direction. Subsequently, by integrating the HCM and LGDM, we designed a fusion gray gradient clutter suppression map (FGGCSM) to effectively enhance the target and suppress clutter from the sea background. Finally, an adaptive constant false alarm threshold is adopted to extract the targets. Extensive experiments on five real infrared maritime image sequences full of sea glints, including a small target and sea–sky background, show that FGGCS effectively increases the signal-to-clutter ratio gain (SCRG) and the background suppression factor (BSF) by more than 22% and 82%, respectively. Furthermore, its receiver operating characteristic (ROC) curve has an obviously more rapid convergence rate than those of other typical detection algorithms and improves the accuracy of small-target detection in complex maritime backgrounds.

Despite DETR-like methods having improved end-to-end detection capabilities, they fundamentally struggle with the uniform processing of entire flattened feature maps, causing queries to attend to irrelevant regions. This results in redundant attention patterns, leading to computational burdens and inefficiencies in the detection of tiny objects in remote sensing imagery across dense and sparse settings. To address these limitations, we present a lightweight Transformer-based encoder-decoder architecture called Dynamic Adaptive Region Transformer (DART). Specifically, Density Adaptive Transformer (DAT) employs an adaptive-region attention (ARA) mechanism that dynamically generates content-aware spatial regions rooted in feature density and semantic relevance. This strategy prioritizes computational resources on semantically rich areas while minimizing focus on irrelevant background regions. Region Aware Decoder (RAD) incorporates a masked region-aware cross-attention (MRA) mechanism, where queries interact exclusively with the adaptive masked regions generated by the encoder, thereby reducing redundant focus on overlapping or irrelevant areas. Meanwhile, a query diversity loss is introduced to penalize overlapping attention patterns among queries, encouraging each query to focus on distinct and complementary regions. By adapting to data density and directing queries to essential areas within the image, DART enhances feature extraction and object localization for various-sized objects in dense and sparse settings. Experimental results demonstrate that DART achieves state-of-the-art performance on AI-TOD, DOTA-v2.0 and LEVIR-Ship benchmarks and exhibits strong generalization capabilities on DIOR, while using only 13 million parameters and a computational cost of 68 GFLOPs.