Infrared small target detection is crucial for infrared search and tracking systems. Saliency-based detection techniques have gradually demonstrated their superiority. Existing methods often adopt a fixed rectangular window for sliding window filtering to measure the saliency of suspected targets, making it difficult to accommodate targets of different sizes and easily leading to contamination by non-target pixels. Therefore, the rectangular sampling window approach increases the difficulty of detecting infrared small targets. In this letter, a novel flexible window local contrast measure (FWLCM) is proposed. Firstly, a flexible window estimation method is suggested that dynamically adjusts the shape of the saliency measurement window based on the heterogeneous variations between the target and the background by performing a flexible region growing. Especially, the local contrast is calculated at an adaptive scale to enhance the target saliency. Secondly, a directional sector local contrast calculation method is introduced to optimize the statistical way of background region characteristics, reducing the interference of discrete noise in the background statistical features. Finally, adaptive threshold segmentation is utilized to extract the real target. Compared with existing methods, experimental results on four real datasets demonstrate the superiority of FWLCM in complex backgrounds.

Infrared small target detection plays a crucial role in fields such as remote sensing and surveillance. However, during long-distance imaging, factors such as atmospheric attenuation lead to a low signal-to-clutter ratio for the targets, making their features difficult to extract effectively. Additionally, in complex background environments, background components that resemble the target morphology highly interfere with detection tasks. Therefore, infrared weak small target detection in complex backgrounds faces challenges of low detection accuracy and high false alarm rates. To solve the above difficulties, a novel entropy variation weighted local contrast measure (EVWLCM) is proposed. Firstly, a target saliency enhancement method based on a family of generalized Gaussian functions is introduced, which accurately characterizes the grayscale distribution states of various targets in infrared images. Secondly, a novel adaptive weighting strategy based on local joint entropy variation characteristics is suggested. Specifically, the spatial grayscale distribution difference between the target and the background is effectively perceived, enhancing the target while suppressing the background. Finally, experimental results on real infrared images show that EVWLCM outperforms existing methods on both public and private datasets. Additionally, the average processing speed of EVWLCM is 34 frames per second, which meets the requirements for real-time scenarios.

Infrared dim and small target detection (IRDSTD) aims to obtain target position information from the background, clutter, and noise. However, for infrared dim and small targets with low signal-to-clutter ratios (SCRs), the detection difficulty lies in the fact that their poor local spatial saliency will lead to missed detections and false alarms. In this work, a spatiotemporally integrated detection network (STIDNet) is proposed for IRDSTD. In the network, a spatial saliency feature generation module (SSFGM) employs a U-shaped network to extract deep features from the spatial dimension of the input image in a frame-by-frame manner and splices them based on the temporal dimension to obtain an airtime feature tensor. IRDSTs with direction-of-motion consistency and strong interframe correlation are reinforced, and randomly generated spurious waves, noise, and other false alarms are inhibited via a fixed-weight multiscale motion feature-based 3D convolution kernel (FWMFCK-3D). A mapping from the features to the target probability likelihood map is constructed in a spatiotemporal feature fusion module (STFFM) by performing 3D convolutional fusion on the spatially localized saliency and time-domain motion features. Finally, several ablation and comparison experiments indicate the excellent performance of the proposed network. For infrared dim and small targets with SCRs < 3, the average AUC value still reached 0.99786.

In recent years, the development of deep learning has significantly improved the performance of infrared small targets detection (IRSTD), particularly in addressing the issue of detecting small infrared targets with high signal-to-clutter ratio (SCR). However, detecting infrared small targets in complex backgrounds with low SCR and poor contrast with the background still faces challenges such as low detection rate and high false alarm rate. In this paper, we propose a novel network called MSGEGA: Multi-Scale Gaussian Enhancement and Global-Aware Network. MSGEGA combines Multi-Scale Gaussian Enhancement with Global-Aware to strengthen the ability to perceive global background features and capture local detailed features of the target. We design infrared small targets feature enhancement module based on multi-scale gaussian templates, which is applied to the head and skip connections to further improve the distinguishability between IRSTs with low-SCR and background. Meanwhile, Global-Aware leverages its global receptive field to suppress background and clutter components with high mutual similarity in images, integrates fine structures and boundary information from different layers, thereby enhancing the local feature representation of IRSTs. To validate the effectiveness of the proposed method, publicly available datasets are filtered to form a specialized low SCR dataset named IRST-LSCR. Extensive experiments show that MSGEGA achieves outstanding performance in detecting small targets with low SCR (SCR < 6). Specifically, the proposed method demonstrates significant advantages on the screened dataset, achieving an AUC of 0.992. At a detection rate of 0.871, it maintains a false alarm rate of 0.9e-5, outperforming all comparison algorithms. The model has 1.17 M parameters and 2.50 GFLOPs, achieving 39.80 FPS on a single RTX 4090.