The detection of infrared small targets under low signal-to-clutter ratio (SCR) and complex background conditions has been a challenging and popular research topic. In this article, a spatial-temporal feature-based detection framework is proposed. First, several factors, such as the infrared target’s small sample, the sensitive size, and the usual sample selection strategy, that affect the detection of small targets are analyzed. In addition, the small intersection over union (IOU) strategy, which helps to solve the false convergence and sample misjudgment problem, is proposed. Second, aiming at the difficulties due to the target’s dim information and complex background, the interframe energy accumulation (IFEA) enhancement mechanism-based end-to-end spatial-temporal feature extraction and target detection framework is proposed. This framework helps to enhance the target’s energy, suppress the strong spatially nonstationary clutter, and detect dim small targets. Experimental results show that using the small IOU strategy and IFEA mechanism, the proposed multiple frame-based detection framework performs better than some popular deep learning (DL)-based detection algorithms.

The operational environment of UAVs poses unique challenges for object detection compared to conventional methods. When UAVs capture remote sensing images from elevated altitudes, objects often appear minuscule and can be easily obscured by complex backgrounds. This increases the likelihood of false positives and missed detections, thereby complicating the detection process. Furthermore, the hardware resources available on UAV platforms are typically highly constrained. To meet deployment requirements, researchers often must compromise some detection accuracy in favor of a more lightweight model. To address these challenges, we propose PS-YOLO, a fast and precise network specifically designed for UAV-based object detection. In the proposed network, we first design a lightweight backbone based on partial convolution. Then, we introduce a more efficient neck network called FasterBIFFPN to replace the original PAFPN, enabling more effective multi-scale feature fusion. Finally, we propose the GSCD head. GSCD employs shared convolutions to enhance the network’s ability to learn common features across objects of different scales and introduces Normalized Gaussian Wasserstein Distance Loss (NWDLoss) to improve detection accuracy. This detection head effectively increases inference speed without significantly increasing parameter counts. The proposed PS-YOLO is validated on the Visdrone2019 dataset, and the results demonstrate that PS-YOLO provides a 2% improvement in precision, 0.5% improvement in recall, 1.3% improvement in mean average precision (mAP), 41.3% reduction in parameter counts, 6.1% reduction in computational cost, and 26.73 FPS improvement in inference speed compared to the benchmark model YOLOv11-s.

The detection and recognition of dim and small infrared (IR) targets across domains pose two formidable challenges: distributional discrepancies in samples and scarcity or absence of annotated instances in the target domain. While current unsupervised domain adaptive object detection methods can somewhat alleviate the performance degradation caused by these issues, they fail to address the differences in semantic content between the background environments in different application scenarios. This results in a semantic gap that impedes the algorithm’s adaptability. We propose a content-decoupled unsupervised domain adaptive method to mitigate the adverse impact of the semantic gap on domain adaptation. Specifically, we introduce an unsupervised task-guided branch in the one-stage detector that executes style transfer in real-time during training, directing the feature representation via shared parameters. A novel content-decoupled feature alignment module aligns semantically unrelated features in the source and target domains while preserving the intrinsic semantics of dim and small IR targets. The experiments on a cross-band IR dataset reveal that the proposed method enhances the mean average precision by 18.3% compared to the baseline detector without increasing computational burden.

Real-time detection of small unmanned aerial vehicle (SUAV) targets in SUAV surveillance systems has become a challenge due to their high mobility, sudden bursts, and small sizes. In this study, we used infrared sensors and Convolutional Neural Networks (CNN)-based detectors to achieve the realtime detection of SUAV targets. Existing object detectors generally suffer from a computational burden or low detection accuracy on small targets, which limits their practicality and further application in SUAV surveillance systems. To solve these problems, we developed a real-time SUAV target detection algorithm based on deep residual networks. In order to improve the sensitivity to small targets, a laterally connected multi-scale feature fusion approach was proposed to fully combine the context features and semantic features. A densely paved pre-defined box with geometric analysis was used for single-stage prediction. Compared with the state-of-the-art object detectors, the proposed method achieved superior performance with respect to average-precision and frames-per-second. As the training set was limited, to improve generalization, we investigate the benefits introduced by data augmentation and data balance, and proposed a weighted augmentation approach. The proposed approach improved the robustness of the detector and the overall accuracy.

Detecting infrared small targets quickly and accurately in complex backgrounds has always been a challenging task. Data-driven methods have achieved good results because of their powerful feature extraction capabilities. Many algorithms use ResNet or VGG as their backbone, but because of the small size and inconspicuous features, pooling layers in their networks could lead to the loss of targets in deep layers. Even though dense network structure is proposed to alleviate this issue, its excessive dense connections makes it difficult to achieve real-time detection. To meet the requirements of both accurate performance and real-time detection, we propose MDAFNet. We design a Moderately Dense Adaptive Feature Fusion (MDAF) module that contains only three feature layers as the backbone of the network. This module connects all internal features with each other and utilizes a weighted sum of different layers as the output, promoting feature reuse and maintaining infrared small target features in the deep layers of the network. We also design a Coarse-to-Fine detection head and introduce auxiliary loss to enable the network to predict target contours with greater precision. Moreover, we propose a new data augmentation method that effectively enhances the generalization performance of network. Experimental results demonstrate that our network achieves excellent performance in detection accuracy and meets the requirements for real-time detection on RTX3080 gpu.

Traditional data-driven algorithms suffer from data reliance, hyperparameter sensitivity, and faint characteristics in infrared (IR) “low, slow, and small” unmanned aerial vehicle target detection and recognition, resulting in performance degradation in complex backgrounds. Inspired by model-driven methods, this article proposes a learnable feature modulation module that uses prior knowledge to enhance feature representation. Specifically, this method converts the local contrast measure into a nonlocal quadrature difference measure in deep feature space, considering feature points that break semantic continuity as the potential target locations through a self-attentive approach. On this basis, considering the scale changes of aircraft targets during radial approach to IR detectors, a multiscale single-stage detector is designed by effective receptive field calculation. In this network structure, a bidirectional serial feature modulation method is used to fully retain the multiscale features of the target and ensure adaptability to point, spot, and area targets while satisfying real-time requirements. The ablation studies verify the effectiveness of each component and help determine the optimal parameter configuration. Finally, comparison experiments with state-of-the-art methods are conducted on a 10k scale IR dataset. The experimental results show that the detection accuracy of this method is better than that of other baseline methods while ensuring real-time performance, especially in highly complex and low-contrast scenes, achieving superior higher target detection accuracy.