Small Infrared Target Detection Research Articles

Infrared Small-Target Detection Using Multidirectional Local Difference Measure Weighted by Entropy

Detecting small targets from infrared remote sensing images is still a challenging task. In this research, we propose a multidirectional local difference measure weighted by entropy (MDLDE) to detect small targets from infrared images with messy backgrounds. First, a new multidirectional local difference measure is proposed to suppress the clutter background. Then, the entropy, which captures the overall heterogeneity between the target and the background, is utilized to enhance the target. Lastly, an adaptive threshold was adopted to segment the target region from the background. The designed MDLDE could effectively enhance the target and simultaneously suppress the background clutter. Experimental results on six datasets indicate that the proposed method outperformed other state-of-the-art methods in terms of the signal-to-clutter ratio gain (SCRG), background suppression factor (BSF), and receiver operating characteristic (ROC) curves.

A double-layer feature fusion convolutional neural network for infrared small target detection

ABSTRACT Infrared small target detection is critical in remote sensing, military, and other fields. However, the low resolution of most infrared images and the lack of texture and detailed information could cause the target to be lost in a relatively noisy background. Therefore, in recent years, researchers have paid particular attention to the problem of small infrared target detection. In this paper, we propose a double-layer feature fusion convolutional neural network for infrared small target detection (DLFF), consisting of a simultaneous upsampling two-layer network module and a ‘T’-type fusion structure. First, the upsampling double-layer network module shares detection information while synchronizing detection, suppressing the background noise and enhancing the detection of the target. In addition, for the small target detection task, since the direct fusion of shallow spatial information and deep semantic information may lose only some small target features, we propose a ‘T’-type fusion structure to solve this problem. Furthermore, we collate an infrared small target dataset (MDFA_SIRIST) and design a pre-processing method for pre-detection images. The experimental results show that our network outperforms the other six state-of-the-art methods in combined evaluation metrics ( -score) and mean intersection ratio (mIou).

Infrared Small-Target Detection Based on Multi-level Local Contrast Measure

Infrared small target detection technology is one of the key technologies for reconnaissance, guidance, and early warning systems, and it has important theoretical and practical value to conduct in-depth research on it. However, there are several challenges in infrared small target detection. Firstly, infrared small targets have low signal-to-noise ratio, which makes them easily submerged in complex backgrounds. Secondly, since infrared small target detection is a long-distance imaging process, there is no shape or texture information available, which increases the difficulty of target detection. To address these challenges, this paper proposes a multi-level contrast enhancement method to suppress structural background, and develops a more effective detection algorithm. Based on the concept of local contrast measurement (LCM), a new contrast-based small target detection algorithm called Multi-Level Local Contrast Measurement (MLLCM) is constructed, and its effective implementation process is provided. Compared with LCM, MPCM (Multiscale Patch-based Contrast Measure), and other algorithms, this algorithm effectively enhances the target area and eliminates background clutter. The results on simulated images demonstrate the effectiveness of this algorithm.

An infrared small target detection model via Gather-Excite attention and normalized Wasserstein distance.

Infrared small target detection (ISTD) is the main research content for defense confrontation, long-range precision strikes and battlefield intelligence reconnaissance. Targets from the aerial view have the characteristics of small size and dim signal. These characteristics affect the performance of traditional detection models. At present, the target detection model based on deep learning has made huge advances. The You Only Look Once (YOLO) series is a classic branch. In this paper, a model with better adaptation capabilities, namely ISTD-YOLOv7, is proposed for infrared small target detection. First, the anchors of YOLOv7 are updated to provide prior. Second, Gather-Excite (GE) attention is embedded in YOLOv7 to exploit feature context and spatial location information. Finally, Normalized Wasserstein Distance (NWD) replaces IoU in the loss function to alleviate the sensitivity of YOLOv7 for location deviations of small targets. Experiments on a standard dataset show that the proposed model has stronger detection performance than YOLOv3, YOLOv5s, SSD, CenterNet, FCOS, YOLOXs, DETR and the baseline model, with a mean Average Precision (mAP) of 98.43%. Moreover, ablation studies indicate the effectiveness of the improved components.

Dim2Clear Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is important for many practical applications such as hazardous aircraft warning, especially when the target is not visible in visible light image due to atmospheric conditions such as fog and cloud. However, IRSTD is challenging due to noises, small and dim targets. To address this challenge, we propose a novel Dim2Clear Network (Dim2Clear) for IRSTD in this paper. Specifically, the Dim2Clear consists of a U-Net backbone encoder, a context mixer decoder (CMD) based on spatial and frequency attention (SFA), and an eyeball-shaped enhancement module (EEM). The CMD is composed of cascaded regular residual blocks where two SFA modules are inserted. Each SFA module receives features from different residual blocks and generates spatial attention map from them to modulate the low-level features, which are then decomposed into low and high frequencies using the discrete cosine transformation. Accordingly, features are further modulated according to the generated frequency attention maps. In this way, SFA can extract both spatial context and frequency context to improve the feature representation capacity. In addition, we design an EEM to suppress the noise and enhance the signal-to-noise ratio in the segmentation results from the perspective of image super-resolution. Experiments on the SIRST dataset and our newly constructed IRSTD-1k dataset show that the proposed Dim2Clear outperforms state-of-the-art methods.

Enhanced Cross-Domain Dim and Small Infrared Target Detection via Content-Decoupled Feature Alignment

Enhanced Cross-Domain Dim and Small Infrared Target Detection via Content-Decoupled Feature Alignment

MSAFFNet: A Multiscale Label-Supervised Attention Feature Fusion Network for Infrared Small Target Detection

The detection of small infrared targets with a low signal-to-noise ratios and contrasts in noisy and cluttered backgrounds is challenging and therefore a domain of active research. Traditional methods result in a large number of false alarms and missed detections. In the case of convolutional neural network-based methods, it may not be possible to identify deep small targets, or the details of the target’s edge contours may not be appropriately considered. Therefore, this paper proposes MSAFFNet to perform infrared small target detection based on an encoder-decoder framework. In the encoder stage, small target features are extracted using a resnet-20 backbone network, and the global contextual features of small targets are extracted using an atrous spatial pyramid pooling module. In the decoding stage, a dual-attention module is used to selectively enhance the spatial details of the target at the shallow level and representative features of the semantic information at the deep level. Multi-scale feature maps are then concatenated to achieve superior feature fusion. Additionally, multi-scale labels are constructed to focus on the details of the target contour and internal features based on edge information and an internal feature aggregation module. Experiments conducted on the NUAA-SIRST, NUDT-SIRST and XDU-SIRST datasets revealed that the proposed approach outperforms the representative methods and achieves an improved detection performance.

Spatial-Temporal Tensor Ring Norm Regularization for Infrared Small Target Detection

Infrared small target detection (ISTD) technique is widely used in infrared searching and tracking (IRST) and military surveillance. Existing detection methods must sufficiently address the challenges of the heterogeneous background with high concealment targets. In this letter, we propose a novel spatial-temporal tensor ring norm regularization (STT-TRNR) to detect infrared small targets. First, to utilize the spatial and temporal context information in a sequence, nonrepetitive spatial-temporal patches are formed by sliding windows in the consecutive frames. The patches are stacked into a tensor structure with spatial and temporal information. Second, the tensor ring nuclear norm is introduced to approximate the rank of the background tensor. The tensor ring regularization improves the correlation between dimensions, protects the internal structure of the tensor, and avoids the dimension disaster caused by train decomposition. Third, the local contrastive feature is used as a priori information to suppress the false alarms caused by the corner edges and other noises and avoid the distortion caused by target movement. Finally, the alternating direction multiplier method (ADMM) is employed to reconstruct the sequence images and retrieve the targets. The experimental results reveal that the suggested model provides improved detection performance and higher robustness across various complicated scene types.

Infrared Small Target Detection Using Spatiotemporal 4-D Tensor Train and Ring Unfolding

Infrared small target detection (ISTD) is vital for civil and military applications. However, existing methods often face challenges in coping with complex scenes, discriminating targets from similar objects, or leveraging temporal information effectively. To tackle these limitations, we offer an innovative approach that exploits the spatio-temporal structure of infrared images. A four-dimensional (4D) infrared tensor is initially constructed from a sequence of infrared images, and decomposed into lower-dimensional tensors using the tensor train (TT) and its extension – tensor ring (TR) techniques. The ISTD problem is then formulated as a sparse plus low-rank decomposition problem, where the sparse part is the target and the low-rank part is the background. We factorize the composed tensors into matrices via TT and TR unfolding approaches, which mitigates the imbalance between different modes containing spatial and temporal information. By constraining the balanced unfolded components with the weighted sum of nuclear norm, we solve the problem using the alternating direction multiplier method (ADMM). Furthermore, we validate models on several datasets and benchmark them with state-of-the-art techniques in detection accuracy and background suppression. Comparison results demonstrate the superiority of our approach over the existing methods. Moreover, the results of an ablation study with three-dimensional (3D) tensor structures show the effectiveness and feasibility of the dimension expansion to 4D.

A Simplified Dual-Weighted Three-Layer Window Local Contrast Method for Infrared Small-Target Detection

In the realm of infrared small-target detection, the weighted local contrast approaches, which seek to improve targets by the defined weighted factors, have garnered a lot of interest. However, there are several problems with these methods as follows. 1) The vast number of local contrast sliding sub-windows restricts the time efficiency. 2) The dim targets in the complicated backgrounds are incorrectly eliminated by the background suppression procedure. 3) The background noise in the complicated environment cannot be effectively muted. A simplified dual-weighted three-layer window local contrast method (SDWTLLCM) is suggested in this work as a solution to these issues. In order to extract tiny targets and suppress complicated backgrounds, a hierarchical convolution filtering window is first created. Then, even without sub-window division, a simple three-layer sliding window is created for time efficiency enhancement. The dual-weighted local contrast approach is also intended to minimize the background and further highlight tiny objects. Eventually, the tiny targets may be extracted more effectively using the adaptive threshold segmentation procedure. The vast experimental findings show that our suggested strategy is effective and efficient.

BPR-Net: Balancing Precision and Recall for Infrared Small Target Detection

BPR-Net: Balancing Precision and Recall for Infrared Small Target Detection

Lightweight Infrared Small Target Detection Network Using Full-Scale Skip Connection U-Net

In order to improve detection performance in a U-net-based IR small target detection (IRSTD) algorithm, it is crucial to fuse low-level and high-level features. Conventional algorithms perform feature fusion by adding a convolution layer to the skip pathway of the U-net and by connecting the skip connection densely. However, with the added convolution operation, the number of parameters of the network increases, hence the inference time increases accordingly. Therefore, in this letter, a UNet3+ based full-scale skip connection U-net is used as a base network to lower the computational cost by fusing the feature with a small number of parameters. Moreover, we propose an effective encoder and decoder structure for improved IRSTD performance. A residual attention block is applied to each layer of the encoder for effective feature extraction. As for the decoder, a residual attention block is applied to the feature fusion section to effectively fuse the hierarchical information obtained from each layer. In addition, learning is performed through full-scale deep supervision to reflect all the information obtained from each layer. The proposed algorithm, coined Attention Multiscale feature Fusion U-net (AMFU-net), can hence guarantee effective target detection performance and a lightweight structure (mIoU: 0.7512, FPS: 86.1). Pytorch implementation is available at: github.com/cwon789/AMFU-net.

Lightweight Multimechanism Deep Feature Enhancement Network for Infrared Small-Target Detection

Specific to the problem of infrared small-target detection in complex backgrounds, a multimechanism deep feature enhancement network model (MDFENet) was proposed. A lightweight multimechanism attention collaborative fusion module was proposed to efficiently fuse low-level features and high-level features to solve the problem that small infrared targets are easy to annihilate in the deep layer of the network. Based on the analysis of the background and target data, a normalized loss function was proposed, which integrates the segmentation threshold selection into the network and normalizes the probability of the network output to simulate a step function and reflect relative differences. Aiming at the sparseness of infrared target features, we used the subpixel convolution method to upsample the features to obtain high-resolution feature images while expanding the size of the feature map. We conducted detailed comparison and ablation experiments, comparing MDFENet with ALCNet, APGCNet, and other state-of-the-art networks to verify the effectiveness and efficiency of the network. Results show that the MDFENet algorithm achieves the optimal result in the balance of detection efficiency and lightweightedness on two datasets.

An Enhanced Image Patch Tensor Decomposition for Infrared Small Target Detection

Infrared small-target detection is a key technology for the infrared search and track system (IRST), but some problems still exist, such as false detections in complex backgrounds and clutter. To solve these problems, a novel image patch tensor (IPT) model for infrared small-target detection is proposed. First, to better estimate the background component, we utilize the Laplace operator to approximate the background tensor rank. Secondly, we combined local gradient features and highlighted area indicators to model the local targets prior, which can effectively suppress the complex background clutter. The proposed model was solved by the alternating direction method of multipliers (ADMM). The experimental results on various scenes show that our model achieves an excellent performance in suppressing strong edge clutter and estimating small targets.

Context-aware cross-level attention fusion network for infrared small target detection

Infrared small target detection (IRSTD) plays an essential role in many fields such as air guidance, tracking, and surveillance. However, due to the tiny sizes of infrared small targets, which are easily confused with background noises and lack clear contours and texture information, how to learn more discriminative small target features while suppressing background noises is still a challenging task. In this paper, a context-aware cross-level attention fusion network for IRSTD is proposed. Specifically, a self-attention-induced global context-aware module obtains multilevel attention feature maps with robust positional relationship modeling. The high-level feature maps with abundant semantic information are then passed through a multiscale feature refinement module to restore the target details and highlight salient features. Feature maps at all levels are fed into a channel and spatial filtering module to compress redundant information and remove background noises, which are then used for cross-level feature fusion. Furthermore, to overcome the lack of publicly available datasets, a large-scale multiscene infrared small target dataset with high-quality annotations is constructed. Finally, extensive experiments on both public and our self-developed datasets demonstrate the effectiveness of the proposed method and the superiority compared with other state-of-the-art approaches.

Multiscale contrast enhancement method for small infrared target detection

Infrared search and track (IRST) systems have been applied extensively in the civil and military fields. However, fast, effective, and robust dim and small infrared target detection remains a challenging research topic. Dim and small targets are easily obscured in different backgrounds or noises, which results in a high false alarm rate. A novel strategy is proposed for small infrared target detection to achieve a high true positive rate and robust detection. First, the prominent edge structures of the original image are obtained by constructing a structure tensor. Subsequently, a Gaussian bandpass filter with multiscale windows is used to obtain an infrared image that contains complex edges and small targets. An infrared image with an enhanced signal-to-clutter ratio is achieved by combining with the two methods, whereby the noise can be effectively reduced. Thereafter, candidate regions that contain the target and a smaller number of edges with higher brightness than that of the target are obtained using relaxed threshold segmentation. Finally, a Gaussian gradient contrast method is proposed in which the highlighted edges are removed so that only small infrared targets remain. Extensive experimental results demonstrate that the proposed method can achieve a significant optimization of the true positive and false alarm rates, as well as improved efficiency and robustness.

A Combined Approach to Infrared Small-Target Detection with the Alternating Direction Method of Multipliers and an Improved Top-Hat Transformation.

In infrared small target detection, the infrared patch image (IPI)-model-based methods produce better results than other popular approaches (such as max-mean, top-hat, and human visual system) but in some extreme cases it suffers from long processing times and inconsistent performance. In order to overcome these issues, we propose a novel approach of dividing the traditional target detection process into two steps: suppression of background noise and elimination of clutter. The workflow consists of four steps: after importing the images, the second step applies the alternating direction multiplier method to preliminarily remove the background. Comparatively to the IPI model, this step does not require sliding patches, resulting in a significant reduction in processing time. To eliminate residual noise and clutter, the interim results from morphological filtering are then processed in step 3 through an improved new top-hat transformation, using a threefold structuring element. The final step is thresholding segmentation, which uses an adaptive threshold algorithm. Compared with IPI and the new top-hat methods, as well as some other widely used methods, our approach was able to detect infrared targets more efficiently (90% less computational time) and consistently (no sudden performance drop).

Infrared Target-Background Separation Based on Weighted Nuclear Norm Minimization and Robust Principal Component Analysis

The target detection ability of an infrared small target detection (ISTD) system is advantageous in many applications. The highly varied nature of the background image and small target characteristics make the detection process extremely difficult. To address this issue, this study proposes an infrared patch model system using non-convex (IPNCWNNM) weighted nuclear norm minimization (WNNM) and robust principal component analysis (RPCA). As observed in the most advanced methods of infrared patch images (IPI), the edges, sometimes in a crowded background, can be detected as targets due to the extreme shrinking of singular values (SV). Therefore, a non-convex WNNM and RPCA have been utilized in this paper, where varying weights are assigned to the SV rather than the same weights for all SV in the existing nuclear norm minimization (NNM) of IPI-based methods. The alternate direction method of multiplier (ADMM) is also employed in the mathematical evaluation of the proposed work. The observed evaluations demonstrated that in terms of background suppression and target detection proficiency, the suggested technique performed better than the cited baseline methods.

When Infrared Small Target Detection Meets Tensor Ring Decomposition: A Multiscale Morphological Framework

Detecting the small targets from a heterogeneous background in an infrared image is a challenging problem, which has received extensive attention. In this article, we propose a method in terms of tensor ring (TR) decomposition and nonlinear multiscale morphological top-hat transformation for infrared small target detection (ISTD). First, a tensor model with prior knowledge is constructed for extracting the structural features of multiple infrared images. Then, the problem of small target detection is converted into a problem of minimizing the tensor rank with TR. Based on the TR decomposition model, we introduce the top-hat regularization into our model with multiple structural elements of different size to perform morphological operations. The corresponding morphological model exploits a more accurate ring top-hat regularization expression through adaptive nonlinear combination for the ISTD problem. Finally, the optimization of the model is realized by the closed solution given by the alternating direction method of multipliers algorithm. In order to verify the superior performance of our method, our method is compared with a number of advanced detection models. By analyzing the results of comparison experiments, the detection accuracy and precision of our model in the detection of small infrared targets have been improved. Even in complex background conditions, our model also maintain a good robustness.

Real-time infrared small target detection network and accelerator design

Infrared small target detection (ISTD) plays an important role in military and civil fields. However, various complex backgrounds in real scenarios significantly degrade the ISTD performance. This study proposes an elaborate convolutional neural network (CNN) for ISTD, called small target (ST)-Net, by modeling the task as an image-to-image translation problem. The input image is translated into a clean output map, where only the target remains. ST-Net is a lightweight network with 13 convolutional layers. It uses residual connections to smoothen the forward and backward propagation. The proposed detection method is validated on two different types of widely used infrared small target datasets. Extensive experiments demonstrate that ST-Net could effectively boost the number of correct detections and reduce the number of missed and false detections. Furthermore, a quantized model training method is presented to find as many circuit design benefits as possible while ensuring that the performance degradation of the model is within an acceptable range. Then a high-throughput and energy-efficient accelerator for ST-Net is designed based on the binarized model. Simulation results under CMOS 28 nm technology show that the proposed accelerator can achieve 56 FPS, which meets the real-time requirement, while the power consumption is as low as 48.7 mW. In addition, the accelerator is implemented on FPGA, which can be deployed into an embedded platform.