Infrared small target detection (IRSTD) plays a vital role in various fields, especially in military early warning and maritime rescue. Its main goal is to accurately locate targets at long distances. Current deep learning-based methods mainly rely on mask-to-mask or box-to-box regression training approaches, making considerable progress in detection accuracy. However, these methods rely on large amounts of training data with expensive manual annotation. Although some researchers attempt to reduce the cost using single-point weak supervision (SPWS), the limited labeling accuracy significantly degrades the detection performance. To address these issues, we propose a novel point-to-point regression high-resolution dynamic network (P2P-HDNet), which can accurately locate the target center using only single-point annotation. Specifically, we first devise the high-resolution cross-feature extraction module (HCEM) to provide richer target detail information for the deep feature maps. Notably HCEM maintains high-resolution throughout the feature extraction process to minimize information loss. Then, the dynamic coordinate fusion module (DCFM) is devised to fully fuse the multidimensional features and enhance the positional sensitivity. Finally, we devise an adaptive target localization detection head (ATLDH) to further suppress clutter and improve the localization accuracy by regressing the Gaussian heatmap and adaptive non-maximal suppression strategy. Extensive experimental results show that P2P-HDNet can achieve better detection accuracy than the state-of-the-art (SOTA) methods with only single-point annotation. In addition, our code and datasets will be available at: https://github.com/Anton-Nrx/P2P-HDNet.

Object detection in remote sensing images has become a crucial component of computer vision. It has been employed in multiple domains, including military surveillance, maritime rescue, and military operations. However, the high density of small objects in remote sensing images makes it challenging for existing networks to accurately distinguish objects from shallow image features. These factors contribute to many object detection networks that produce missed detections and false alarms, particularly for densely arranged objects and small objects. To address the above problems, this paper proposes a feature enhancement feedforward network (FEFN), based on a lightweight channel feedforward module (LCFM) and a feature enhancement module (FEM). First, the FEFN captures shallow spatial information in images through a lightweight channel feedforward module that can extract the edge information of small objects such as ships. Next, it enhances the feature interaction and representation by utilizing a feature enhancement module that can achieve more accurate detection results for densely arranged objects and small objects. Finally, comparative experiments on two publicly challenging remote sensing datasets demonstrate the effectiveness of the proposed method.

Infrared Small-Object Detection (IRSTD) presents a unique training challenge: objects typically span less than 0.01% of image pixels, so background gradients overwhelm the optimization process and compressed models tend to lose object information. It is found that this imbalance is significantly mitigated in the spectral domain—in the intermediate network layer, the gradient ratio of the target to the background reaches more than 30 times the observed value in the spatial domain. Building upon this observation, we present HiSGD (High–Low Spectral Gradient Decoupling), a principled framework that operates on local differential spectra rather than global feature maps. HiSGD introduces a Spectral Gradient Decoupling Operator (SGDO) that deterministically separates high-frequency target anomalies from low-frequency background stationarity via Differential Gaussian Decoupling (DGD)—a scale-aware local decomposition that matches the receptive field of typical IR small targets. The decoupled high-frequency band is supervised by an Adaptive Sparse-Edge Gradient Modulation (ASEGM) loss, which implicitly rebalances gradient contributions through an adaptive discrepancy sensitivity function without requiring explicit spatial masks. The low-frequency band is constrained by a Background Semantic Coherence Loss (BSCL) that enforces second-order statistical consistency rather than pixel-wise fidelity, preventing overfitting to sensor noise. By selectively distilling layers where spectral target contrast peaks, HiSGD enables lightweight student architectures that reduce parameters and FLOPs by 93% relative to their teachers while outperforming existing distillation methods on NUDT-SIRST and NUAA-SIRST. On the Jetson AGX Orin edge device, the distilled models reduce inference latency by 59.5% and 20.3%, validating the practical effectiveness of our approach for edge deployment.

Infrared small target detection (IRSTD) tasks are extremely challenging for two main reasons: 1) it is difficult to obtain accurate labelling information that is critical to existing methods, and 2) infrared (IR) small target information is easily lost in deep networks. To address these issues, we propose a single-point supervised high-resolution dynamic network (SSHD-Net). In contrast to existing methods, we achieve state-of-the-art (SOTA) detection performance using only single-point supervision. Specifically, we first design a high-resolution cross-feature extraction module (HCEM), that achieves bi-directional feature interaction through stepped feature cascade channels (SFCC). It balances network depth and feature resolution to maintain deep IR small-target information. Secondly, the effective integration of global and local features is achieved through the dynamic coordinate fusion module (DCFM), which enhances the anti-interference ability in complex backgrounds. In addition, we introduce the high-resolution multilevel residual module (HMRM) to enhance the semantic information extraction capability. Finally, we design the adaptive target localization detection head (ATLDH) to improve detection accuracy. Experiments on the publicly available datasets NUDT-SIRST and IRSTD-1k demonstrate the effectiveness of our method. Compared to other SOTA methods, our method can achieve better detection performance with only a single point of supervision.