Infrared Guidance System Research Articles

Fine-Grained Guided Model Fusion Network with Attention Mechanism for Infrared Small Target Segmentation

Infrared small target segmentation plays an important role in infrared guidance systems. In this paper, a fine-grained guided model fusion network with attention mechanism (FAMNet) is proposed for improving the performance of the infrared small target segmentation. An autonomous traditional feature extraction algorithm based on information entropy and four-directional gradient contrast is proposed for solving the difficulty of feature extraction from small targets. The deep features are extracted from an improved U-Net. The two kinds of features are fused by channel shuffle, which could enhance the communication efficiency between the channels of the two features. More context information from the target and background is introduced into the network by cross layer parallelization convolutional block attention module (CLPCBAM). Compared with some state-of-the-art techniques, the proposed FAMNet performs significantly better in terms of IoU, nIoU, and F1-score comprehensively.

Improved YOLOv5 infrared tank target detection method under ground background

The detection precision of infrared seeker directly affects the guidance precision of infrared guidance system. To solve the problem of low target detection accuracy caused by the change of imaging scale, complex ground background and inconspicuous infrared target characteristics when infrared image seeker detects ground tank targets. In this paper, a You Only Look Once, Transform Head Squeeze-and-Excitation (YOLOv5s-THSE) model is proposed based on the YOLOv5s model. A multi-head attention mechanism is added to the backbone and neck of the network, and deeper target features are extracted using the multi-head attention mechanism. The Cross Stage Partial, Squeeze-and-Exclusion module is added to the neck of the network to suppress the complex background and make the model pay more attention to the target. A small object detection head is introduced into the head of the network, and the CIoU loss function is used in the model to improve the detection accuracy of small objects and obtain more stable training regression. Through these several improvement measures, the background of the infrared target is suppressed, and the detection ability of infrared tank targets is improved. Experiments on infrared tank target datasets show that our proposed model can effectively improve the detection performance of infrared tank targets under ground background compared with existing methods, such as YOLOv5s, YOLOv5s + SE, and YOLOV 5 s + Convective Block Attention Module.

Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background

Infrared point-target detection is one of the key technologies in infrared guidance systems. Due to the long observation distance, the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering, and the signal-to-noise ratio is low. On the other hand, the target in the image appears in the form of fuzzy points, so that the target has no obvious features and texture information. Therefore, scholars have proposed many object detection methods for dimming infrared images, which has become a hot research topic on account of the flow-rank model based on the image patch. However, the result has a high false alarm rate because the most low-rank models based on the image patch do not consider the spatial-temporal characteristics of the infrared sequences. Therefore, we introduce 3D total variation (3D-TV) to regularize the foreground on account of the non-convex rank approximation minimization method, so as to consider the spatial-temporal continuity of the target and effectively suppress the interference caused by dynamic background and target movement on the foreground extraction. Finally, this paper proposes the minimization of the non-convex spatial-temporal tensor low-rank approximation algorithm (MNSTLA) by studying the related algorithms of the point infrared target detection, and the experimental results show strong robustness and a low false alarm rate for the proposed method compared with other advanced algorithms, such as NARM, RIPT, and WSNMSTIPT.

Human vision-inspired detection for infrared small targets under scaly light background on sea surface

The automatic detection and search of small targets by the infrared guidance system is challenged by the complex sea background environment, particularly the dense scaly light generated by the reflection of sunlight by the sea clutter and the appearance of boundaries. To address this issue, inspired by the significant sensitivity, classification perception, reverse exclusion, and association prediction characteristics of the human visual system, this paper proposes a human vision-inspired detection model for infrared small targets under scaly light background on the sea surface. Firstly, the local energy factor and multi-scale patch contrast measure are fused to construct a significance analysis model and enhance the contrast of the target region of interest. Then, the image is classified into different regions according to the detection of grayscale fluctuations and mutation points in the background, and a region self-classification perception model is constructed for adaptive segmentation of scene images. Next, the prior information of the target gradient is used to construct a reverse exclusion analysis model for suppressing the residual background of the boundary. Subsequently, by using the fluctuation law and characteristic difference of the spatial energy and size features of the target and the scaly light background in the time domain, the correlation prediction analysis model is constructed to further suppress the residual background. Finally, through the combination of the above four visual feature models, a new detector with good adaptability is proposed for small targets under scaly light background on the sea surface. The experimental results indicate that the average detection rate of our detector is 95.26%, and the number of false alarms in a single frame is reduced by 93.77% compared with the typical detectors.

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.

Infrared Small Target Detection Using Homogeneity-Weighted Local Contrast Measure

Detecting small targets in infrared (IR) image sequences is an important task in IR guidance systems. The clutter of complex backgrounds often submerges small targets, making detection difficult. Achieving high detection and low false alarm rates with complex backgrounds is a primary problem. We propose an IR small target detection method using our new homogeneity-weighted local contrast measure (HWLCM). Inspired by the ability of the human visual system (HVS) to determine saliency characteristics, we implement our method to use the local contrast features of the central and surrounding regions and the weighted homogeneity characteristics of the surrounding regions to enhance the target while suppressing the complex background. Our method divides each image into blocks with a sliding window for which the HWLCM is calculated. The HWLCM enhances the actual target and suppresses interference simultaneously. We apply an adaptive threshold to target region extraction to further refine the results. Our experimental results show that our proposed method is more effective than six comparable methods, especially in terms of the signal-to-clutter gain (SCRG) and background suppression factor (BSF) indicators.

Infrared Signature Suppression Systems in Modern Military Helicopters

Abstract Helicopters play an important role in air-to-ground fire covering and the short-distance air-to-air fights, as well as the anti-tank missions and battlefield force transferring. The detection and survivability of helicopters on a battlefield significantly depends on their infrared emissions level, as well as the methods, equipment and systems used by potential enemy. The automatic detection systems, recognition and identification of flying objects use among other the thermo-detection methods, which rely on detecting the infrared radiation emitted by the tracked object. Furthermore, due to low-altitude and relatively low flight speed, today’s combat assets like missile weapons equipped with infrared guidance systems are one of the most important threats to the helicopters performing combat missions. Especially meaningful in a helicopter aviation is infrared emission by exhaust gases, egressed to the surroundings. Due to high temperature, exhaust gases are a major factor in detectability of a helicopter performing air combat operations. In order to increase the combat effectiveness and survivability of military helicopters, several different types of the infrared suppressor (IRS) have been developed. This paper reviews contemporary developments in this discipline, with particular examples of the infrared signature suppression systems.

Elastic‐viscoplasticity modeling of the thermo‐mechanical behavior of chalcogenide glass for aspheric lens molding

AbstractChalcogenide glass (ChG), as an alternative material in place of single‐crystal germanium, is increasingly used in thermal imaging, night vision, and infrared guidance systems, etc., owing to their excellent formability through precision glass molding (PGM). The deformation mechanisms of these glasses at the molding temperature involve elasticity, plasticity, and viscous flow, which call for a new theoretical model to assist the design of PGM process. This paper investigates the thermo‐mechanical properties of Ge22Se58As20 at the temperature above its softening point and establishes a new elastic‐viscoplasticity model to describe its thermo‐mechanical behaviors. After determining the model parameters through cylindrical compression tests, the new constitutive model is implemented in finite element method (FEM) of PGM to form an aspheric ChG lens. And the agreement of displacement‐time curves between experimental and simulation results exhibit the validity of the proposed elastic‐viscoplastic constitutive model.

Multiscale patch-based contrast measure for small infrared target detection

Infrared (IR) small target detection plays an important role in IR guidance systems. In this paper, a biologically inspired method called multiscale patch-based contrast measure (MPCM) is proposed for small IR target detection. MPCM can increase the contrast between target and background, which makes it easy to segment small target by simple adaptive thresholding method. Experimental results on three sequences demonstrate that the proposed small target detection method can not only suppress background clutter effectively even if with strong noise interference, but also detect targets accurately with low false alarm rate and high speed.

Design and Research of Hardware-in-the Loop Platform of Infrared Seeker Based on Lab-VIEW

With the development of infrared missile guidance system fleetly, the simulation plays more and more important role in the integrated testing of infrared missile guidance system, because it is a key technology to shorten manufacture period and to advance efficiency-cost ratio. This paper designs hardware-in-the-loop simulation platform of infrared guidance system, including: the simulation modeling of kinematics equation of characteristics, the generation technology of target background and antagonistic, the key technology design of hardware and software are researched thoroughly about signal resting, their constructions are discussed in detail.

Modeling and Simulation for the Infrared Guidance System

The specific guidance simulation system for the infrared air-to-air missile is built. Seeker model including the target detection and tracking loop has simulated not only the different modes of field of view (FOV) according to the geometrical relationship between the target and missile, but also the jamming effect based on the equivalent target calculation; fuze model contains three different trigger mechanisms; guidance of missile is realized via three-dimensional proportional guidance law; the 6 DOF kinematics model and dynamic model of airframe are derived, through aerodynamic force and torque calculating, triple-channel autopilot model is set up by using the PID control method. Finally simulations under three different cases are implemented and the results show that the whole system is credible.