Infrared small target detection plays a crucial role in infrared search and tracking systems. However, current detection methods are limited by the small target size and low signal-to-noise ratio of infrared imagery. Furthermore, motion features for target detection are difficult to extract using simple frame subtraction due to poor imaging conditions. Therefore, we focus on the holistic interframe interaction to enhance the temporal feature and propose a spatiotemporal local contrast method in this letter. First, the motion-enhanced density peak clustering is employed to determine the robust localization of candidate targets, in which the density feature maps are generated by the preprocessing of non-consecutive three frames difference after image registration. Second, to reliably exploit interframe interactions across both non-consecutive and successive frames, a temporal domain saliency map is computed based on local regions from successive frames. Moreover, a spatial domain saliency map is obtained using a novel tri-layer local contrast measure. By fusing results from both domains, the infrared small targets are detected through adaptive threshold segmentation. Experimental results on four real sequences demonstrate that the proposed method can achieve better detection performance by target enhancement and background suppression than other spatiotemporal algorithms.

Detecting infrared small targets from complex background is a challenging task. Due to the low signal-to-noise ratio and few pixels of targets, it is difficult to get accurate edge segmentation, and the targets are easily mixed up by adjacent region. To overcome these problems, we propose a gradient-enhanced feature pyramid network (GEFPN) in this letter. Specifically, we first generate gradient information under the assistance of supplementary gradient enhancement (SGE) branch, which is conductive to highlight gradient magnitude and mitigate the inaccurate edge location of small targets. On the basis of this, the proposed network utilizes a dilated cross-stage partial module (DCSPM) to refine the multiscale features and encode supplemental gradient information into the main FPN structure. Moreover, we construct a patch attention fusion module (PAFM), which fully collects both spatial details and semantic information. The experimental results show that the proposed GEFPN can achieve excellent detection performance with mean intersection over union (IoU) reaching 0.939 and 0.732 on public NUDT-SIRST and SIRST-Aug datasets, respectively, and with 0.42 M parameters and inference speed of 67.47 FPS. The code of GEFPN is available at:https://github.com/xiyunqiao/irst3.

Detecting tiny objects in remote sensing images has been an intriguing yet challenging topic in the remote sensing image processing. While significant progress has been made in many studies, most existing methods focus on improving the accuracy of tiny object detection without particular consideration for computational complexity, which restricts their applicability in resource-limited condi-tions. Therefore, this paper aims to design a lightweight de-tection algorithm tailored for tiny objects in remote sensing images. First, we investigate the impact of the complexity of different components in deep learning-based object detec-tion models on the accuracy of tiny object detection, includ-ing the backbone and detection head. Then, a dedicated backbone for tiny object detection is proposed, achieving competitive detection accuracy while remaining lightweight. Moreover, we propose a lightweight detection head that in-corporates deformable convolution and optimize the chan-nel dimension. Finally, we combine the above methods to introduce a lightweight network, LTDNet, for tiny object detection in remote sensing images. Benefiting from the dedicated designs for the backbone and detection head spe-cifically for tiny objects, the proposed method can achieve competitive detection accuracy with very low parameters and computational complexity. Extensive experiments are conducted on the AI-TODv2 and LEVIR-Ship datasets, and the results demonstrate the effectiveness of our proposed method. Specifically, the proposed method achieves 54.6% AP50 on the AI-TODv2 dataset with only 4.85M parameters and 38.19G FLOPs. The code will be released soon on the site of https://github.com/dyl96/LTDNet.

Infrared small target suffers from weak features and complex background. The existing detection methods are usually unable to effectively extract global context and maintain features of infrared small targets. To overcome the problems, we design a non-local enhanced feature pyramid network (NLFPNet) based on an encoder-decoder framework by additionally global contextual features modeling. In the encoder stage, small targets features are extracted by a ResNet-18 based backbone network. Then, global contextual information of small targets is exploited by using a non-local enhanced pyramid pooling module (NLPPM), which is conductive to estimate the correlation between pixels in a wide range and enhance the global prior information. In the decoder stage, we obtain multi-level feature representation through asymmetric attention fusion module (AAFM), which reasonably modulates and maintains the shallow-level spatial details and deep-level semantic information. The experimental results show that the proposed NLFPNet can achieve an improved detection performance with mean intersection over union (IoU) of 0.739 and probability of detection (Pd) of 0.953 on the public SIRST-Aug dataset. The code is available at https://github.com/xiyunqiao/irst1.

Infrared small target detection (ISTD) has been widely concerned in certain fields like astronomy, surveillance, and missile early warning system. ISTD is still a challenging task due to the complex backgrounds and small size of targets, which restrict the performance of the convolutional neural networks (CNN) in ISTD. To this end, a dual branch architecture which combines nested attention network and auxiliary transformer enhancement (NANetFormer) is proposed. The CNN-based branch uses channel-spatial-attention embedded U-Net++ architecture to obtain low-level local details of small targets and suppress background noises. The transformer-based branch applies hierarchical self-attention mechanism as an auxiliary enhancement encoder path. Furthermore, we design a local-global feature fusion module to make feature concentration of two branches. Experimental results show that proposed network achieves competitive results compared with other state-of-the-art methods.