Small Infrared Target Research Articles

Novel Asymmetric Pyramid Aggregation Network for Infrared Dim and Small Target Detection

Robust and efficient detection of small infrared target is a critical and challenging task in infrared search and tracking applications. The size of the small infrared targets is relatively tiny compared to the ordinary targets, and the sizes and appearances of the these targets in different scenarios are quite different. Besides, these targets are easily submerged in various background noise. To tackle the aforementioned challenges, a novel asymmetric pyramid aggregation network (APANet) is proposed. Specifically, a pyramid structure integrating dual attention and dense connection is firstly constructed, which can not only generate attention-refined multi-scale features in different layers, but also preserve the primitive features of infrared small targets among multi-scale features. Then, the adjacent cross-scale features in these multi-scale information are sequentially modulated through pair-wise asymmetric combination. This mutual dynamic modulation can continuously exchange heterogeneous cross-scale information along the layer-wise aggregation path until an inverted pyramid is generated. In this way, the semantic features of lower-level network are enriched by incorporating local focus from higher-level network while the detail features of high-level network are refined by embedding point-wise focus from lower-level network, which can highlight small target features and suppress background interference. Subsequently, recursive asymmetric fusion is designed to further dynamically modulate and aggregate high resolution features of different layers in the inverted pyramid, which can also enhance the local high response of small target. Finally, a series of comparative experiments are conducted on two public datasets, and the experimental results show that the APANet can more accurately detect small targets compared to some state-of-the-art methods.

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).

Robust small infrared target detection using multi-scale contrast fuzzy discriminant segmentation

Infrared images with complex cloudy-sky and sea-sky backgrounds have low contrast and a low signal-to-clutter ratio (SCR), which makes it extremely challenging for conventional small target detection methods to accurately detect small targets. This study proposes a novel method for detecting small infrared targets by using spectral, spatial, and temporal information to improve the detection performance. First, to achieve background suppression and target enhancement, the proposed approach implements a parallel reconstruction with the spectral information to generate the fusion saliency map. Then, multi-scale contrast fuzzy discriminant segmentation (MCFDS) is used to measure the certainty of the target boundary using the spatial information. The detection task is regarded as a fuzzy discriminant segmentation problem, which can determine the target boundary accurately. Subsequently, position prediction with optical flow estimation and forward pipeline filtering are adopted to achieve multi-frame target recognition and reduce the false alarm rate effectively. The proposed algorithm is evaluated on real infrared thermal image sequences of small targets against complex cloudy-sky and sea-sky backgrounds. The results show that it can achieve stable, continuous, and robust detection for different target sizes and SCR values as well as for multiple targets and/or background types compared with conventional baseline methods, such as top-hat, ILCM, PQFT, MPCM and TM-SCR. In addition, mathematical proofs are provided to elucidate the proposed approach.

Infrared dim and small targets detection via self-attention mechanism and pipeline correlator

Detecting dim and small infrared targets (DSIT) is always the most concerned and challenging problem in the field of infrared target detection, due to the small size and lacking of high-level semantic information. Benefiting from the great success of deep learning methods in other fields, many superior methods have been proposed in the field of infrared target detection. Based on the analysis of the distributions of a large number of DSIT, we explore to detect DSIT through heatmaps which is more competitive in terms of computational cost and detection performance than using anchor boxes or segmentation maps. Following this line, we design a novel DSIT detection method while adopts the semantic-level and the pixel-level self-attention mechanisms, and improve the Adaptive Pipeline Correlator (APC) in the multi-frame filtering tracking. We evaluate the proposed method on the single-frame database for Infrared Small Target detection (SIRST) and sequence-frame database for Dim Small Infrared target tracking (DIRST). The results show that our method achieves the recall rate and the accuracy rate higher than 98% on SIRST. It takes about 1/9 of the computational cost of the Single Shot MultiBox Detector for Small Target (SSDST) series methods and achieves better results than them on DIRST. When cascading with APC, it achieves an excellent performance with the recall rate of 92.8% and the accuracy rate of 97.9%. All these results fully prove that the proposed detection method is competitive for detecting DSIT.

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.

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking.

Infrared small target detection using kernel low-rank approximation and regularization terms for constraints

The robust detection of infrared (IR) small targets remains a challenging task. When targets are submerged in heavy clutter or disturbed by strong edge, it is difficult to extract targets and suppress background clutter simultaneously. In this paper, we present a kernel robust principal component analysis model for IR small target detection. Firstly, to overcome the interference of heavy clutter and strong edge, the kernel low-rank approximation is proposed to estimate the background component; Then, to further improve detection performance, the lp-norm is adopted to recover the sparse component, the total variation regularization is employed to smooth the background, and the graph Laplacian regularization is utilized as a sparse constraint to constrain the homogeneous target region. Finally, we derive an effective method to solve the proposed model based on the alternating direction method of multipliers. Experimental results not only show the proposed method can robustly and effectively detect IR small targets even when the targets are submerged in strong clutter or seriously disturbed by strong edge, but also confirm the proposed method is competitive with the state-of-the-art methods.

From characteristic response to target edge diffusion: An approach to small infrared target detection

Small Infrared (IR) target detection plays an important role in flight guidance and target early warning. Most existing solutions we call local contrast (LC) based methods utilize the local contrast between the targets and the background to detect small infrared targets. This paper designs a characteristic response curve (CRC) to demonstrate characteristics of LC-based methods and proposes a multi-scale target edge diffusion model (M-TED) for small infrared target detection. First, considering the edge diffusion phenomenon on small infrared targets, the transition region is integrated with the target and the background regions to measure local contrast. In addition, global information can be used to suppress the background. Next, a customized multi-scale strategy based on the CRC is applied to generate an appropriate response map. Finally, small targets are detected via segmentation of the response map. Experiments show that the CRC-customized multi-scale strategy can significantly improve LC-based methods’ performance and our M-TED performs best on various evaluations.

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection.

Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.

Infrared Small Target Detection Based on Weighted Local Coefficient of Variation Measure.

Robust infrared (IR) small target detection is critical for infrared search and track (IRST) systems and is a challenging task for complicated backgrounds. Current algorithms have poor performance on complex backgrounds, and there is a high false alarm rate or even missed detection. To address this problem, a weighted local coefficient of variation (WLCV) is proposed for IR small target detection. This method consists of three stages. First, the preprocessing stage can enhance the original IR image and extract potential targets. Second, the detection stage consists of a background suppression module (BSM) and a local coefficient of variation (LCV) module. BSM uses a special three-layer window that combines the anisotropy of the target and differences in the grayscale distribution. LCV exploits the discrete statistical properties of the target grayscale. The weighted advantages of the two modules complement each other and greatly improve the effect of small target enhancement and background suppression. Finally, the weighted saliency map is subjected to adaptive threshold segmentation to extract the true target for detection. The experimental results show that the proposed method is more robust to different target sizes and background types than other methods and has a higher detection accuracy.

Infrared Small Target Detection Using Regional Feature Difference of Patch Image

Aiming at a thorny issue, that conventional small target detection algorithm using local contrast method is not sensitive for residual background clutter, robustness of algorithms is not strong. A Gaussian fusion algorithm using multi-scale regional patch structure difference and Regional Brightness Level Measurement is proposed. Firstly, Regional Energy Cosine (REC) is constructed to measure the structural discrepancy among a small target with neighboring cells. At the same time, Regional Brightness Level Measurement (RBLM) is constructed utilizing the brightness difference characteristics between small target and background areas. Then, a brand new Gaussian fusion algorithm is proposed for the generated saliency map in multi-scale space to characterize the overall heterogeneity in original infrared small target and local neighborhood. Finally, a self-adapting separation algorithm is adopted with the objective to obtain a small target from background interference. This method is able to utmostly restrain background interference and enhance the target. Extensive qualitative and quantitative testing results display that the desired algorithm has remarkable performance in strengthening target region and restraining background interference compared with current algorithms.

Infrared small target detection based on gray intensity descent and local gradient watershed

Robust infrared (IR) small target detection is an essential part of infrared search and tracking systems. Existing IR target detection methods based on human visual system usually use statistical features of rectangular region to measure the difference between target and background. In the case that the dim target size is smaller than the rectangular window cell size, since the window cell contains both the target and the background pixels, the statistical features cannot well represent the target contrast. The use of a rectangular window structure for calculation reduces the statistical difference between the target and the background, that is, reduces the saliency of the target, resulting in a slow rise in the ROC curve of the HVS method. This paper proposes a novel non-window structure filter for small target detection. Firstly, we obtain candidate target points by combining the high-frequency information of the image with the local maximum points, and at the same time, calculate the Sobel gradient map to prepare for subsequent processing. Then, a filter is designed based on gray intensity descent and local gradient watershed (GID-LGW) characteristics of the target, and it is used to calculate the contrast of each candidate target point. Finally, an adaptive threshold operation is applied to extract the target. Experiments show that compared with several baseline methods, the proposed method has a better detection rate, lower false alarm, and high speed, especially for small and weak targets with a size smaller than 3 × 2.

Infrared Image Small-Target Detection Based on Improved FCOS and Spatio-Temporal Features

The research of infrared image small-target detection is of great significance to security monitoring, satellite remote sensing, infrared early warning, and precision guidance systems. However, small infrared targets occupy few pixels and lack color and texture features, which make the detection of small infrared targets extremely challenging. This paper proposes an effective single-stage infrared small-target detection method based on improved FCOS (Fully Convolutional One-Stage Object Detection) and spatio-temporal features. In view of the simple features of infrared small targets and the requirement of real-time detection, based on the standard FCOS network, we propose a lightweight network model combined with traditional filtering methods, whose response for small infrared targets is enhanced, and the background response is suppressed. At the same time, in order to eliminate the influence of static noise points in the infrared image on the detection of small infrared targets, time domain features are added to the improved FCOS network in the form of image sequences, so that the network can learn the spatio-temporal correlation features in the image sequence. Finally, compared with current typical infrared small-target detection methods, the comparative experiments show that the improved FCOS method proposed in this paper had better detection accuracy and real-time performance for infrared small targets.

Double layer local contrast measure and multi-directional gradient comparison for small infrared target detection

Infrared small target detection is one of the key technologies in the search and track (IRST) based on infrared imaging equipment. At present, the performance of small target detection based on single frame infrared image is directly related to the accuracy of subsequent target tracking, so it has been studied a lot. However, the existing small target detection algorithms have certain limitations in detection accuracy and real-time performance, especially when the contrast between the target and the background area is not high or the background is complex, especially in the complex sea or sky background, due to the influence of a large amount of noise and clutter in the background, the existing infrared small target detection algorithms have a high false alarm rate. To solve the above problems, this paper proposes a small target detection algorithm based on weighted double layer local contrast and multi-directional gradient map, which realizes the accurate detection of small targets from two aspects of targets’ local contrast and gradient. Firstly, we design an improved two layer local contrast measurement architecture, and use the weighted mean method to better represent the gray value of the local window; Secondly, a local contrast comparison method based on target and background is proposed to enhance the intensity of small targets and suppress some background clutter; Then, the multi-directional gradient map is used to further suppress the noise so as to improve the contrast between the target and the background. At the same time, singular value decomposition (SVD) method is used to extract the main features including small targets, which can effectively suppress the small texture interference around the targets in the background without losing the target intensity; Finally, an adaptive threshold method is used to separate small targets from their background. Experimental results show that compared with the existing algorithms, the proposed detection algorithm can effectively reduce the false alarm rate in different complex scenes, and the computational efficiency is improved compared with some multi-scale small target detection methods. At the same time, the signal to clutter ratio (SCR), background suppression factor (BSF) and receiver operating characteristic (ROC) curve are also better than these existing state of the art algorithms, which can display good robustness.

Infrared Small Target Detection Network With Generate Label and Feature Mapping

In the complex background, the contrast and signal-to-noise (SNR) ratio of the infrared (IR) small target are low. Therefore, the traditional IR small target detection algorithms are difficult to achieve good detection performance when the characteristics of small targets are sparse. To solve this problem, an IR small target detection network with generate label and feature mapping (GLFM)-net is proposed in this letter. First, in the GLFM-net model, a scale adaptive feature extraction network is proposed for the IR small target sparse features extraction, and then, the multilayer joint upsampling feature mapping network is proposed for small target feature mapping and background suppression. Based on this model, the feature mapping results of IR dim and small targets with the greatly suppressed background are obtained. Second, in model training, we designed a 2-D Gaussian label generation strategy for the problem of sample imbalance, which can achieve excellent detection performance by using small training samples. The experimental results show that the network can detect IR small targets with different sizes and low SNRs in various complex backgrounds and has good effectiveness and robustness compared with the existing algorithms.

Total Variation-Based Interframe Infrared Patch-Image Model for Small Target Detection

Infrared (IR) small target detection is one of the most fundamental techniques in the infrared search and track (IRST) system. Due to the interferences caused by background clutter and image noise, conventional IR small target detection algorithms always suffer from a high false alarm rate and are unable to achieve robust performance in complex scenes. To accurately distinguish IR small target from the background, we propose a total variation (TV)-based interframe infrared patch-image model that regards the long-distance IR small target detection task as an optimization problem. First, the input IR image is converted to a patch-image that consists of a sparse target matrix and a low-rank background matrix. Then, the interframe similarity of target appearance is utilized to impose a temporal consistency constraint on the target matrix. Next, a TV regularization term is proposed to further alleviate the false alarms generated by noise. Finally, an alternating optimization algorithm using singular value decomposition (SVD) and accelerated proximal gradient (APG) is designed to mathematically solve the proposed model. Both qualitative and quantitative experiments implemented on real IR sequences demonstrate that our model outperforms other traditional IR small target methods in terms of the signal-to-clutter ratio gain (SCRG) and the background suppression factor (BSF).

Small Infrared Target Detection Based on Fast Adaptive Masking and Scaling With Iterative Segmentation

Fast and robust small infrared (IR) target detection is a challenging task and critical to the performance of IR searching and tracking (IRST) systems. However, the current algorithms generally have difficulty in striking a good balance between speed and performance. In this letter, we propose a new approach to small IR target detection that can significantly accelerate the detection process by first performing a fast adaptive masking and scaling algorithm. We then propose to enhance the target characteristics and suppress the background clutter using both contrast and gradient information. Finally, we propose to accurately extract the targets via iterative segmentation. The experimental results demonstrated that our proposed method yields the best and the most robust performance, with a speed of at least two times faster than the state-of-the-art methods.

STTM-SFR: Spatial–Temporal Tensor Modeling With Saliency Filter Regularization for Infrared Small Target Detection

Detecting small Infrared (IR) targets against the low-altitude complex background is always a challenge for Infrared search and tracking (IRST) system due to limited small target characteristics, the moving background caused by camera motion, and extremely cluttered backgrounds. The existing methods usually cause high false alarm or do not work against the chaotic low-altitude complex background. In this paper, a novel spatial-temporal tensor model with saliency filter regularization (STTM-SFR) is developed to detect small IR targets. Firstly, the small target detection task is transformed into a sparse and low-rank tensor optimization problem by using the spatial-temporal prior knowledge of background and target. The construction of the holistic spatial-temporal tensor model (STTM) can retain the complete spatial-temporal information of the original IR image sequence. Then, the saliency filter regularization term limited between background and foreground aims to promote target saliency learning. That is to say, the saliency filter regularization term can avoid the offset approximation of the low-rank tensor, so as to recover a clean target image from the original IR tensor. Finally, an effective alternating direction method of multipliers (ADMM) algorithm framework is designed to solve the proposed STTM-SFR model. The effectiveness and robustness of STTM-SFR model are verified in six real IR scenes. Experimental results show that our method outperforms other baseline methods. Moreover, the proposed STTM-SFR method is more robust than the existing state-of-the-art STTMs against low-altitude moving backgrounds.

Infrared Small Target Detection Utilizing Halo Structure Prior-Based Local Contrast Measure

Infrared (IR) small target detection under low signal-to-clutter ratio (SCR) is a fundamental and important problem in some critical missions like IR search and tracking (IRST). Though there exist a lot of works using local contrast measure (LCM) to detect the target, their performance are still unsatisfying due to the imperfect knowledge of target structure. In this letter, a novel IR small target detection method utilizing halo structure prior (HSP)-based LCM (HSPLCM) is proposed, which adequately considers the structure characteristic of the target. Specifically, through weighting the raw IR image via image structure tensor, we put forward a simple but useful image prior (named as HSP) which reflects the unique structural feature of the target to distinguish the real target and other background clutters. Afterward, based on this prior an effective LCM method is constructed to detect the IR small target, which can enhance the target and suppress the background clutters simultaneously. Furthermore, we extend the proposed algorithm to its multiscale version to solve the target scale uncertainty issues. Extensive experimental results have demonstrated that our proposed method favorably outperforms the state-of-the-arts.