Small Infrared Target Detection Research Articles

SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection

Infrared small target detection (IRSTD) is the process of recognizing and distinguishing small targets from infrared images that are obstructed by crowded backgrounds. This technique is used in various areas, including ground monitoring, flight navigation, and so on. However, due to complex backgrounds and the loss of information in deep networks, infrared small target detection remains a difficult undertaking. To solve the above problems, we present a shallow feature fusion network (SFFNet) based on detection framework. Specifically, we design the shallow-layer-guided feature enhancement (SLGFE) module, which guides multi-scale feature fusion with shallow layer information, effectively mitigating the loss of information in deep networks. Then, we design the visual-Mamba-based global information extension (VMamba-GIE) module, which leverages a multi-branch structure combining the capability of convolutional layers to extract features in local space with the advantages of state space models in the exploration of long-distance information. The design significantly extends the network’s capacity to acquire global contextual information, enhancing its capability to handle complex backgrounds. And through the effective fusion of the SLGFE and VMamba-GIE modules, the exorbitant computation brought by the SLGFE module is substantially reduced. The experimental results on two publicly available infrared small target datasets demonstrate that the SFFNet surpasses other state-of-the-art algorithms.

ILN-SSR: Improved Logarithmic Norm and Sparse Structure Refinement for Infrared Small Target Detection

The effective discrimination of targets from backgrounds in environments characterized by a low signal-to-clutter ratio (SCR) is paramount for the advancement of infrared small target detection (IRSTD). In this work, we propose a novel detection framework predicated on low-rank sparse decomposition (LRSD), incorporating an improved logarithmic norm and a mechanism for sparse structure refinement, herein referred to as the improved logarithmic norm and sparse structure refinement (ILN-SSR). The ILN-SSR framework more precisely characterizes the sparse properties of both the background and the target, enabling a more effective distinction between the target and its background. Initially, our approach entails the utilization of an improved logarithmic norm to precisely estimate the low-rank attributes of the infrared image background. This is followed by the employment of a linear sparse regularization term alongside a target-traits-based sparse regularization term aimed at meticulously identifying targets within sparse regions and refining the sparse structure. Subsequently, we combine these components into the ILN-SSR framework, which formulates IRSTD as an optimization problem. The resolution of this framework is achieved through the implementation of the alternating direction method of multipliers (ADMM). The efficacy of the proposed framework is corroborated through the analysis of six image sequences. Comprehensive experimental assessments affirmed the framework’s substantial robustness in navigating various complex backgrounds.

5-D spatial–temporal information-based infrared small target detection in complex environments

Recently, infrared (IR) small target detection problem has attracted increasing attention. Tensor component analysis-based techniques have been widely utilized, while they are faced with challenges such as tensor structures, background and target estimation, and real-time performance. In this paper, we propose a 5-D spatial–temporal factor-based completion model (5D-STFC) for IR small target detection. Specifically, a 5-D whitened spatial–temporal patch-tensor is constructed. Then, we devise a spatial–temporal factor-based low-rank background estimation norm and a Moreau envelope-derived sparsity estimation norm based on joint spatial–temporal knowledge. Furthermore, we establish a comprehensive completion model for component analysis. To efficiently solve this model, we design a multi-block alternating direction method of multipliers (multi-block ADMM)-based optimization scheme. Extensive experiments conducted on five real IR sequences demonstrate the superiority of 5D-STFC over nine state-of-the-art competitive methods. It can be concluded that 5D-STFC is excellent and practical in target detectability, background suppressibility, overall performance, and real-time performance.

Advancing primate surveillance with image recognition techniques from unmanned aerial vehicles.

Using unmanned aerial vehicles (UAVs) for surveys on thermostatic animals has gained prominence due to their ability to provide practical and precise dynamic censuses, contributing to developing and refining conservation strategies. However, the practical application of UAVs for animal monitoring necessitates the automation of image interpretation to enhance their effectiveness. Based on our past experiences, we present the Sichuan snub-nosed monkey (Rhinopithecus roxellana) as a case study to illustrate the effective use of thermal cameras mounted on UAVs for monitoring monkey populations in Qinling, a region characterized by magnificent biodiversity. We used the local contrast method for a small infrared target detection algorithm to collect the total population size. Through the experimental group, we determined the average optimal grayscale threshold, while the validation group confirmed that this threshold enables automatic detection and counting of target animals in similar datasets. The precision rate obtained from the experiments ranged from 85.14% to 97.60%. Our findings reveal a negative correlation between the minimum average distance between thermal spots and the count of detected individuals, indicating higher interference in images with closer thermal spots. We propose a formula for adjusting primate population estimates based on detection rates obtained from UAV surveys. Our results demonstrate the practical application of UAV-based thermal imagery and automated detection algorithms for primate monitoring, albeit with consideration of environmental factors and the need for data preprocessing. This study contributes to advancing the application of UAV technology in wildlife monitoring, with implications for conservation management and research.

PHSI-RTDETR: A Lightweight Infrared Small Target Detection Algorithm Based on UAV Aerial Photography

To address the issues of low model accuracy caused by complex ground environments and uneven target scales and high computational complexity in unmanned aerial vehicle (UAV) aerial infrared image target detection, this study proposes a lightweight UAV aerial infrared small target detection algorithm called PHSI-RTDETR. Initially, an improved backbone feature extraction network is designed using the lightweight RPConv-Block module proposed in this paper, which effectively captures small target features, significantly reducing the model complexity and computational burden while improving accuracy. Subsequently, the HiLo attention mechanism is combined with an intra-scale feature interaction module to form an AIFI-HiLo module, which is integrated into a hybrid encoder to enhance the focus of the model on dense targets, reducing the rates of missed and false detections. Moreover, the slimneck-SSFF architecture is introduced as the cross-scale feature fusion architecture of the model, utilizing GSConv and VoVGSCSP modules to enhance adaptability to infrared targets of various scales, producing more semantic information while reducing network computations. Finally, the original GIoU loss is replaced with the Inner-GIoU loss, which uses a scaling factor to control auxiliary bounding boxes to speed up convergence and improve detection accuracy for small targets. The experimental results show that, compared to RT-DETR, PHSI-RTDETR reduces model parameters by 30.55% and floating-point operations by 17.10%. Moreover, detection precision and speed are increased by 3.81% and 13.39%, respectively, and mAP50, impressively, reaches 82.58%, demonstrating the great potential of this model for drone infrared small target detection.

AIMED-Net: An Enhancing Infrared Small Target Detection Net in UAVs with Multi-Layer Feature Enhancement for Edge Computing

In the context of small unmanned aerial vehicles (UAVs), infrared imaging faces challenges such as low quality, difficulty in detecting small targets, high false alarm rates, and computational resource constraints. To address these issues, we introduce AIMED-Net, an enhancing infrared small target detection net in UAVs with multi-layer feature enhancement for edge computing. Initially, the network encompasses a multi-layer feature enhancement architecture for infrared small targets, including a generative adversarial-based shallow-feature enhancement network and a detection-oriented deep-feature enhancement network. Specifically, an infrared image-feature enhancement method is proposed for the shallow-feature enhancement network, employing multi-scale enhancement to bolster target detection performance. Furthermore, within the YOLOv7 framework, we have developed an improved object detection network integrating multiple feature enhancement techniques, optimized for infrared targets and edge computing conditions. This design not only reduces the model’s complexity but also enhances the network’s robustness and accuracy in identifying small targets. Experimental results obtained from the HIT-UAV public dataset indicate that, compared to YOLOv7s, our method achieves a 2.5% increase in F1 score, a 6.1% rise in AP for detecting OtherVehicle targets, and a 2.6% improvement in mAP across all categories, alongside a 15.2% reduction in inference time on edge devices. Compared to existing state-of-the-art approaches, our method strikes a balance between detection efficiency and accuracy, presenting a practical solution for deployment in aerial edge computing scenarios.

TFCD-Net: Target and False Alarm Collaborative Detection Network for Infrared Imagery

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. Detecting small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches mainly focus on modeling targets while often overlooking false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features at multiple scales and using gradual upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across different scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets and produce the final detection result. Experiments conducted on public datasets show that our model achieves the highest and second-highest scores for the IoU, Pd, and AUC and the lowest Fa among the DNN methods. These results demonstrate that our model accurately segments targets while effectively extracting false alarm sources, which can be used for further studies.

TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection

Infrared small target detection (ISTD) is critical to national security and has been extensively applied in military areas. ISTD aims to segment small target pixels from background. Most ISTD networks focus on designing feature extraction blocks or feature fusion modules, but rarely describe the ISTD process from the feature map evolution perspective. In the ISTD process, the network attention gradually shifts towards target areas. We abstract this process as the directional movement of feature map pixels to target areas through convolution, pooling and interactions with surrounding pixels, which can be analogous to the movement of thermal particles constrained by surrounding variables and particles. In light of this analogy, we propose Thermal Conduction-Inspired Transformer (TCI-Former) based on the theoretical principles of thermal conduction. According to thermal conduction differential equation in heat dynamics, we derive the pixel movement differential equation (PMDE) in the image domain and further develop two modules: Thermal Conduction-Inspired Attention (TCIA) and Thermal Conduction Boundary Module (TCBM). TCIA incorporates finite difference method with PMDE to reach a numerical approximation so that target body features can be extracted. To further remove errors in boundary areas, TCBM is designed and supervised by boundary masks to refine target body features with fine boundary details. Experiments on IRSTD-1k and NUAA-SIRST demonstrate the superiority of our method.

IRPruneDet: Efficient Infrared Small Target Detection via Wavelet Structure-Regularized Soft Channel Pruning

Infrared Small Target Detection (IRSTD) refers to detecting faint targets in infrared images, which has achieved notable progress with the advent of deep learning. However, the drive for improved detection accuracy has led to larger, intricate models with redundant parameters, causing storage and computation inefficiencies. In this pioneering study, we introduce the concept of utilizing network pruning to enhance the efficiency of IRSTD. Due to the challenge posed by low signal-to-noise ratios and the absence of detailed semantic information in infrared images, directly applying existing pruning techniques yields suboptimal performance. To address this, we propose a novel wavelet structure-regularized soft channel pruning method, giving rise to the efficient IRPruneDet model. Our approach involves representing the weight matrix in the wavelet domain and formulating a wavelet channel pruning strategy. We incorporate wavelet regularization to induce structural sparsity without incurring extra memory usage. Moreover, we design a soft channel reconstruction method that preserves important target information against premature pruning, thereby ensuring an optimal sparse structure while maintaining overall sparsity. Through extensive experiments on two widely-used benchmarks, our IRPruneDet method surpasses established techniques in both model complexity and accuracy. Specifically, when employing U-net as the baseline network, IRPruneDet achieves a 64.13% reduction in parameters and a 51.19% decrease in FLOPS, while improving IoU from 73.31% to 75.12% and nIoU from 70.92% to 74.30%. The code is available at https://github.com/hd0013/IRPruneDet.

MUPT-Net: Multi-scale U-shape pyramid transformer network for Infrared Small Target Detection

Infrared Small Target Detection (IRSTD) aims to detect small and dim targets in complex backgrounds. However, the low signal-to-noise ratio and reduced contrast in the infrared domain make it challenging to extract these targets, as the cluttered background can easily overpower them. Existing Convolutional Neural Networks (CNN)-based methods for IRSTD often suffer from information loss due to inadequate utilization of acquired information after downsampling operations. This limits their ability to accurately extract shape information related to infrared small targets. To address this challenge, we propose a Multi-scale U-shape Pyramid Transformer Network (MUPT-Net). Our network incorporates the U-shape Interaction Module (UIM) and the Multi-scale ViT Module (MSVM) to perform feature extraction. By fully leveraging and integrating the information obtained after each downsampling operation, our approach enables precise extraction of shape information for infrared small targets. Additionally, we introduce the Axial Compression Attention module (ACA), which focuses on capturing the interplay of positional information within the feature map to facilitate accurate detection of small targets. Through iteratively fusing and augmenting multi-scale features, our MUPT-Net effectively assimilates and harnesses contextual information of small targets. Experimental results on the SIRST v1, SIRST v2 and NUDT-SIRST datasets demonstrate the superiority of our approach compared to representative state-of-the-art (SOTA) IRSTD methods.

ISTD-diff: Infrared Small Target Detection via Conditional Diffusion Models

ISTD-diff: Infrared Small Target Detection via Conditional Diffusion Models

Towards Dense Moving Infrared Small Target Detection: New Datasets and Baseline

Towards Dense Moving Infrared Small Target Detection: New Datasets and Baseline

Hybrid Network Based on Hierarchical Multipatch Feature Encoder for Infrared Small Target Detection

Hybrid Network Based on Hierarchical Multipatch Feature Encoder for Infrared Small Target Detection

Enhanced Generalization Ability of Infrared Small Target Detection via Perturbed Training and Adaptive Test

Enhanced Generalization Ability of Infrared Small Target Detection via Perturbed Training and Adaptive Test

Hollow Side Window Filter With Saliency Prior for Infrared Small Target Detection

Hollow Side Window Filter With Saliency Prior for Infrared Small Target Detection

IRGraphSeg: Infrared Small Target Detection Based on Hierarchical GNN

IRGraphSeg: Infrared Small Target Detection Based on Hierarchical GNN

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Differential Attention Orientated Cascade Network for Infrared Small Target Detection

Differential Attention Orientated Cascade Network for Infrared Small Target Detection

A Non-Local Enhanced Feature Pyramid Network for Infrared Small Target Detection

A Non-Local Enhanced Feature Pyramid Network for Infrared Small Target Detection

FATCNet : Feature Adaptive Transformer and CNN for Infrared Small Target Detection

FATCNet : Feature Adaptive Transformer and CNN for Infrared Small Target Detection