Infrared Target Detection Method Research Articles

USES-Net: An Infrared Dim and Small Target Detection Network with Embedded Knowledge Priors

Detecting and identifying small infrared targets has always been a crucial technology for many applications. To address the low accuracy, high false-alarm rate, and poor environmental adaptability that commonly exist in infrared target detection methods, this paper proposes a composite infrared dim and small target detection model called USES-Net, which combines the target prior knowledge and conventional data-driven deep learning networks to make use of both labeled data and the domain knowledge. Based on the typical encoder–decoder structure, USES-Net firstly introduces the self-attention mechanism of Swin Transformer to replace the universal convolution kernel at the encoder end. This helps to extract potential features related to dim, small targets in a larger receptive field. In addition, USES-Net includes an embedded patch-based contrast learning module (EPCLM) to integrate the spatial distribution of the target as a knowledge prior in the training network model. This guides the training process of the constrained network model with clear physical interpretability. Finally, USES-Net also designs a bottom-up cross-layer feature fusion module (AFM) as the decoder of the network, and a data-slicing-aided enhancement and inference method based on Slicing Aided Hyper Inference (SAHI) is utilized to further improve the model’s detection accuracy. An experimental comparative analysis shows that USES-Net achieves the best results on three typical infrared weak-target datasets: NUAA-SIRST, NUDT-SIRST, and IRSTD-1K. The results of the target segmentation are complete and sufficient, which demonstrates the validity and practicality of the proposed method in comparison to others.

Infrared small target detection based on multi-perception of target features

The rapid and efficient detection of infrared dim and small targets in complex backgrounds is a critical area of research in infrared image processing. When confronted with limited target features, it becomes vital to mine and construct additional features in order to accurately differentiate the target from the surrounding background and noise. This paper presents a novel small infrared target detection method based on multi-perception of target features which involves four main stages. First, two new target features are constructed to reduce the similarity between the target and noise and smooth the background. Second, two designed filters are applied to enhance the significance of the target and achieve preliminary detection. Third, structure tensor analysis is used to remove the significant background edge regions and perceive targets with strong corner characteristics. Finally, pseudo targets are eliminated by utilizing target candidate areas to achieve the final detection. Experimental results demonstrate that this method is faster and more robust compared to existing detection algorithms, with superior adaptability and detection performance.

Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices

Existing target detection algorithms for infrared road scenes are often computationally intensive and require large models, which makes them unsuitable for deployment on edge devices. In this paper, we propose a lightweight infrared target detection method, called Edge-YOLO, to address these challenges. Our approach replaces the backbone network of the YOLOv5m model with a lightweight ShuffleBlock and a strip depthwise convolutional attention module. We also applied CAU-Lite as the up-sampling operator and EX-IoU as the bounding box loss function. Our experiments demonstrate that, compared with YOLOv5m, Edge-YOLO is 70.3% less computationally intensive, 71.6% smaller in model size, and 44.4% faster in detection speed, while maintaining the same level of detection accuracy. As a result, our method is better suited for deployment on embedded platforms, making effective infrared target detection in real-world scenarios possible.

Infrared Target Detection Method Based on Attention Mechanism

Infrared Target Detection Method Based on Attention Mechanism

Local Spatial-Temporal Matching Method for Space-Based Infrared Aerial Target Detection.

The feature of a space-based infrared signal is that the intensity of clutter is much stronger than that of an aerial target. Such a feature poses a great challenge to aerial target detection since the existing infrared target detection methods are prone to enhance clutter but ignore the real target, which results in missed detection and false alarms. To tackle the challenge, we propose a concise method based on local spatial–temporal matching (LSM). Specifically, LSM mainly consists of local normalization, local direction matching, spatial–temporal joint model, and inverse matching. Local normalization aims to enhance the target to the same strength as the clutter, so that the weak target will not be ignored. After normalization, a direction-matching step is applied to estimate the moving direction of the background between the basic frame and referenced frame. Then the spatial–temporal joint model is constructed to enhance the target and suppress strong clutter. Similarly, inverse matching is conducted to further enhance the target. Finally, a salience map is obtained, on which the aerial target is extracted by the adaptive threshold segmentation. Experiments conducted on four space-based infrared datasets indicate that LSM handles the above challenge and outperforms seven state-of-the-art methods in space-based infrared aerial target detection.

Ground infrared target detection method based on a parallel attention mechanism (Invited)

Ground infrared target detection method based on a parallel attention mechanism (Invited)

Low Contrast Infrared Target Detection Method Based on Residual Thermal Backbone Network and Weighting Loss Function

Infrared (IR) target detection is an important technology in the field of remote sensing image application. The methods for IR image target detection are affected by many characteristics, such as poor texture information and low contrast. These characteristics bring great challenges to infrared target detection. To address the above problem, we propose a novel target detection method for IR images target detection in this paper. Our method is improved from two aspects: Firstly, we propose a novel residual thermal infrared network (ResTNet) as the backbone in our method, which is designed to improve the feature extraction ability for low contrast targets by Transformer structure. Secondly, we propose a contrast enhancement loss function (CTEL) that optimizes the weights about the loss value of the low contrast targets’ prediction results to improve the effect of learning low contrast targets and compensate for the gradient of the low-contrast targets in training back propagation. Experiments on FLIR-ADAS dataset and our remote sensing dataset show that our method is far superior to the state-of-the-art ones in detecting low-contrast targets of IR images. The mAP of the proposed method reaches 84% on the FLIR public dataset. This is the best precision in published papers. Compared with the baseline, the performance on low-contrast targets is improved by about 20%. In addition, the proposed method is state-of-the-art on the FLIR dataset and our dataset. The comparative experiments demonstrate that our method has strong robustness and competitiveness.

IYOLO: Multi-scale infrared target detection method based on bidirectional feature fusion

Convolutional neural networks are advanced computer vision solution for various tasks, and have made considerable progress. In order to detect infrared targets with details and texture features, we propose a detection method based on YOLOv3. First, we propose a bidirectional feature fusion structure called Improve-FPN(Im-FPN), which allows fast and efficient multi-scale feature fusion. Second, we use the Focal Loss(FL) loss function to balance the problem of sample imbalance. Based on these optimizations, an Infrared-YOLO (IYOLO) target detection model for infrared target is constructed. On the self-constructed infrared dataset: Infrared-VOC, the mAP of IYOLO can achieve 77.1%, which is 4.0% higher than YOLOv3, and the detection speed can reach 55.6FPS. Experimental results show that this method can improve the overall accuracy of detection while ensuring real-time detection.

An infrared target intrusion detection method based on feature fusion and enhancement

Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning. In view of the characteristics of intrusion targets as well as inspection difficulties, an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed. This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels. Among them, LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library, while motion frame difference method can detect the moving regions of the image, improve the integrity of target regions such as camouflage, sheltering and deformation. In order to integrate the advantages of the two methods, the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced. The enhancement module of the network strengthened and screened the targets, and realized the background suppression of infrared images. Based on the experiments, the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated, and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets, and it’s detection performance was far better than the comparison algorithm. The experiment results indicated that, compared with traditional infrared target detection methods, the proposed method could detect the infrared invasion target more accurately, and suppress the background noise more effectively.

IR Target Detection via Lateral Inhibition and Singular Value Decomposition

IR (infrared) target detection has been an important technology in the field of target search and tracking. Generally, due to the influence of IR detector noise, cloud interference and other factors, IR image is blurred, the contrast is low, and the background clutter is heavy. As a result, detecting IR targets from complex background has become a challenging task, especially when the target is small, dim and shapeless. Meanwhile, when detecting and tracking a moving IR target, the method should be able to detect both small target and area target. In this paper, an IR target detection method via LI (lateral inhibition) and SVD (singular value decomposition) is proposed. Firstly, a local structure descriptor based on SVD of gradient domain is constructed, which reflects the basic structures of the local regions of an IR image. Then, combining with the local structure descriptor, a modified LI network is established to enhance target and suppress background. Meanwhile, to calculate lateral inhibition coefficients adaptively, the direction parameters of LI network are determined according to the dominant orientations obtained from SVD. Experimental results show that compared with the typical methods, the proposed method not only can detect small and area target under complex backgrounds, but also has excellent abilities of background suppression and target enhancement.

Infrared Small Target Detection Based on Morphology and SUSAN Algorithm

Infrared small target detection is one of the key techniques in infrared imaging guidance system. The technology of infrared small target detection still needs to be further studied to improve the detection performance. This paper combines the high-pass filtering characteristics of morphological top-hat transform with SUSAN algorithm, and proposes a small infrared target detection method based on morphology and SUSAN algorithm. This method uses top-hat transform to detect the high-frequency region in infrared image, and filters out the low-frequency region in the image to implement the preliminary background suppression of infrared image. Then the SUSAN algorithm is used to detect small targets in the image after background suppression. The proposed method is applied to the single infrared image which is acquired by the infrared guidance system in the process of detecting and tracking the target under specific conditions. The experimental results show that the method is effective and can detect infrared small targets under different background.

Point spread character based low SNR single pixel infrared target detection

Robust and efficient low signal-to-noise ratio (SNR) single pixel infrared (IR) target detection has been a key technique in IR searching and tracking system. However, there exist two critical difficulties, i.e. noise disturbance and low contrast between target and background, influencing detection accuracy. A novel IR target detection method is proposed to overcome those difficulties, by utilising the point-spread character. Specifically, a point-spread indicator is advocated to protect the potential target with the median filter to suppress the noise. Afterwards, a two-step enhancement model is built to increase the contrast value between target region and surrounding background effectively. In the first step, a point-spread local contrast method is proposed to enhance targets and suppress background. In the second step, a high boost filter is introduced to further enhance the target. In addition, an adaptive segmentation method is exploited to extract targets from the enhanced image. Experimental results on testing sequences demonstrate that this method outperforms the other state-of-the-art methods in single pixel IR target detection with higher detection rate, lower false alarm rate and less time consuming.

Infrared target detection method based on the receptive field and lateral inhibition of human visual system

In this paper, an infrared target adaptive detection method based on the receptive field and lateral inhibition (LI) of the human visual system is proposed. In the proposed method, the direction parameters of a Gabor filter are adaptively determined according to the gradient direction, so that edges in the image can be detected without manual intervention. Meanwhile, background prediction based on LI is used for regulating the gray value in the image to achieve background suppression and target enhancement. Experimental results indicate that the proposed method can extract both the small target and the area target from a complex background, and has satisfactory target detection ability.

Infrared small target detection based on relevance vector regression

It is the core task of space combat operations to detect and track targets, and the infrared target detection technology has an immediate impact on the performance of combat systems. So, the infrared target detection has been one of the emphases in military research. With the accelerated development of modern high-tech, combat systems are required more in their abilities of long-range target detection and tracking. It needs us to explore much more precise infrared target detection methods to achieve better target detection performance. To complete this challenging task, an infrared small target detection method based on Relevance Vector Regression (RVR) is proposed in this paper according to the characteristics of infrared target and related theories of RVR. Firstly, the basic theories and related techniques of RVR are introduced. Secondly, the specific method of infrared target detection based on RVR and nonlinear kernel correlation coefficient is reviewed. Thirdly, we proposed the new detection method based on RVR. In the last part, the experiment results which compared the new method with other methods are shown. The final experiment results prove that the proposed method has validity and better performance than the classical compared methods.

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.

Confidence-driven infrared target detection

The confidence of target detection can be used to evaluate the reliability and risk level of the detected targets and can effective help to exclude the false alarms, but very little investigation was involved in the past. In this letter, a confidence-driven infrared target detection method is proposed. We develop three confidence evaluating methods: (1) the median classification confidence of the cascade classifier; (2) the context confidence based on the number and the confidence of the merged detection rectangles around the detected target; and (3) the contrast confidence based on the difference between the detected target distribution and the around background distribution. The three confidences are combined to form the final confidence of the detected targets. We then use the confidence to refine the localization of the targets. The evaluation using real infrared images demonstrates the good performance of the proposed confidence-driven infrared detection algorithm on both undetected error and false alarm.

Image layering based small infrared target detection method

An image layering and confidence analysis based small target detection method in infrared image is proposed. First, a Huffman tree is used to refine the histogram curve, and the valleys of the refined curve are detected automatically. Then, the grey values of the detected valleys are recorded as the segmenting thresholds to stratify the original infrared image. After detecting small abnormal regions in each layer and defining them to be candidate targets, the candidate target set is composed of the candidate targets in all layers. Finally, the abnormality-based confidence of each candidate target is calculated and sorted. The candidate target with maximum confidence is considered as the real one. The experiments show that the proposed method performs robustly and efficiently.

Analysis of small infrared target features and learning-based false detection removal for infrared search and track

An infrared search and track system is an important research goal for military applications. Although there has been much research into small infrared target detection methods, we cannot apply them in real field situations due to the high false alarm rate caused by clutter. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. In our study, eight target features were extracted and analyzed statistically. Learning-based classifiers, such as SVM and Adaboost, have been incorporated and then compared to conventional classifiers using real infrared images. In addition, the generalization capability has also been inspected for various types of infrared clutter.