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.

The data-driven InfraRed Small Target Detection (IRSTD) methods have witnessed remarkable advancements in performance. However, these methods typically rely on high-quality pixel-level mask labels, requiring substantial human effort and time for annotation. To tackle this challenge, we propose a Prior Knowledge Enhanced Learning Approach (PKELA), which contains two key strategies: the Prior Knowledge Enhanced Initial Pseudo-label Generation (PKEIPG) strategy and the Teacher Knowledge Guided Label Update (TKGLU) strategy. Specifically, for the PKEIPG strategy, we introduce a Local Contrast Enhancement (LCE) module based on prior knowledge to suppress background interference. Drawing inspiration from the human brain’s processing sequence of visual information, we locate an appropriate neighborhood containing the infrared small target by identifying the target’s edges. Within this neighborhood, high-quality initial pseudo labels are obtained through adaptive threshold segmentation, which provide strong supervisory signals for the data-driven IRSTD method during the initial training phase. For the TKGLU strategy, we employ an Exponential Moving Average (EMA) teacher model that dynamically updates its parameters based on the current model’s state. Furthermore, a memory bank is established to archive the teacher model’s performance, which is treated as prior knowledge. This knowledge is then systematically used to guide the refinement of pseudo labels. Experimental results on the SIRST, NUDT-SIRST, IRSTD-1k, and SIRST3 datasets indicate that our PKELA exhibits superior training performance with single-point labels. By incorporating our approach, the data-driven IRSTD methods could achieve near-full-supervised performance in terms of Probability of Detection (Pd) metric, with Intersection over Union (IoU) and normalized Intersection over Union (nIoU) reaching up to 95.75% and 95.82% of full-supervised performance, respectively. Our code is available at https://gitee.com/mynewspace/pkela.

Due to the lack of color and texture information and the fuzzy boundary of infrared (IR) small targets, the pixel-level mask annotation process consumes a lot of manual cost and is difficult to achieve accurate annotation. To further reduce the annotation burden, we propose an IR small target mask generation algorithm based on single-point supervised multi-scale chain growth clustering (MCGC). The core of this work is the adaptive generation of IR small-target pseudo mask maps under the supervision of randomly given single-point labels, sequentially through the strategies of multi-scale chain growth, Euclidean coefficient decay, K-Means clustering, and eight-neighborhood clustering. On the four public datasets, ablation experiments, qualitative and quantitative comparison experiments demonstrate that the MCGC algorithm has an efficient and accurate IR small target pseudo mask generation capability, which can be adapted to different numbers, scales, shapes, and intensities of targets in complex backgrounds. In addition, IR-Labelmask, an IR small target mask annotation software designed based on the MCGC algorithm, is publicly available on kourenke/IR-Labelmask-software (github.com). To our knowledge, this is the first mask annotation software designed for IR small target.

Arbitrary-oriented object detection is vital for improving UAV sensing and has promising applications. However, challenges persist in detecting objects under extreme conditions like low-illumination and strong occlusion. Cross-modal feature fusion enhances detection in complex environments but current methods do not adequately learn the features of each modality for the current environment, resulting in degraded performance. To tackle this, we propose the CRSIOD network that effectively learns diverse sensor image features to capture distinct scenarios and target characteristics. Firstly, we design an illumination perception module to guide the object detection network in performing various feature processing tasks. Secondly, to leverage the respective advantages of two modalities and mitigate their negative impacts, we introduce an uncertainty aware module to quantify the uncertainties present in each modality as weights to motivate the network to learn in a direction favorable for optimal object detection. Moreover, in the object detection network, we design a two-stream backbone network based on the attention mechanism to enhance the learning of difficult samples, utilize the CMAFF module to fully extract the shared and complementary features between the two modalities, and design a three-branch feature enhancement network to enhance the learning of the three modal features separately. Finally, to optimize detection results, we design light perception non-maximum suppression and improve the horizontal detection head to a rotating one to preserve object orientation. We evaluate the proposed method CRSIOD on the Drone Vehicle dataset of public UAV aerial images. Compared with the existing commonly used methods, CRSIOD achieves state-of-the-art detection performance.

To solve the problem of infrared (IR) small target tracking loss or error caused by factors such as scale changes, motion blur, occlusion, etc., this paper proposes a multi-strategy fusion tracking algorithm using an IR small target segmentation network as the detection head, which mainly includes six strategies: target pixel clustering, target feature threshold adjustment, large area search, small area tracking, gate tracking, and coordinate solution. First, candidate targets are obtained through the IR small target segmentation network and pixel clustering strategy. Second, the range of candidate targets is further reduced through threshold adjustment strategies. Then, real-time tracking of IR small targets is achieved through large area search, small area tracking, and wave gate tracking strategies. Finally, the longitude, latitude, and altitude of the tracked target are obtained through coordinate calculation strategies. Both qualitative and quantitative experiments based on real IR small target sequences verify that our algorithm can achieve more satisfactory performances in terms of success rate, precision, and robustness compared with other typical visual trackers. In addition, we have deployed tracking algorithms on the Orange Pi 5 embedded platform, and the tracking speed meets the real-time requirements.

Efficiently and accurately separating infrared (IR) small targets from complex backgrounds presents a significant challenge. Numerous studies in the literature have proposed various feature fusion modules designed specifically to enhance the extraction of IR small target features. While these designs offer some incremental improvement to the accuracy of IR small target detection, they come at a steep cost of significantly increasing network parameters and FLOPs. Striving for a balance between computational efficiency and model accuracy, we decided to forgo these complex feature fusion modules. Instead, we developed a new lightweight encoding and decoding structure known as the Lightweight IR Small Target Segmentation Network (LW-IRSTNet). This structure integrates regular convolutions, depthwise separable convolutions, atrous convolutions, and asymmetric convolutions modules. In addition, we devised post-processing modules including an eight-neighborhood clustering algorithm and an online target feature adjustment strategy. Experimental results indicate that: 1) the segmentation accuracy metrics of LW-IRSTNet match the best results of 14 state-of-the-art comparative baselines; 2) the parameters and FLOPs of LW-IRSTNet, at only 0.16M and 303M respectively, are significantly smaller in comparison to these baselines; and 3) the post-processing modules enhance both user-friendliness and the robustness of algorithm deployment. Moreover, LW-IRSTNet has been successfully implemented on both embedded platforms and websites, expanding its range of applications. Utilizing the ONNX framework, NPU acceleration, and CPU multi-threaded resource allocation, we have been able to achieve high-performance inference capabilities, as well as online dynamic threshold adjustment with the LW-IRSTNet. The source codes for this project can be accessed at https://github.com/kourenke/LW-IRSTNet.