Infrared Imaging Guidance Research Articles

Infrared Image Generation Based on Visual State Space and Contrastive Learning

The preparation of infrared reference images is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared data on-site is difficult and time-consuming. Fortunately, the infrared images can be obtained from the corresponding visible-light images to enrich the infrared data. To this end, this present work proposes an image translation algorithm that converts visible-light images to infrared images. This algorithm, named V2IGAN, is founded on the visual state space attention module and multi-scale feature contrastive learning loss. Firstly, we introduce a visual state space attention module designed to sharpen the generative network’s focus on critical regions within visible-light images. This enhancement not only improves feature extraction but also bolsters the generator’s capacity to accurately model features, ultimately enhancing the quality of generated images. Furthermore, the method incorporates a multi-scale feature contrastive learning loss function, which serves to bolster the robustness of the model and refine the detail of the generated images. Experimental results show that the V2IGAN method outperforms existing typical infrared image generation techniques in both subjective visual assessments and objective metric evaluations. This suggests that the V2IGAN method is adept at enhancing the feature representation in images, refining the details of the generated infrared images, and yielding reliable, high-quality results.

Anti-Interference Recognition for Aerial Infrared Object Based on Convolutional Feature Inference Network

The infrared decoy countermeasure technology develops along with the infrared imaging guidance technology. The release of infrared decoys can block the object and destroy the integrity of the object features. The traditional statistical approach based on feature fusion matching for pattern recognition is not effective in handling this problem. To improve the characterization ability of the invariant detail features in the infrared object domain and improve the classification accuracy of the probability identification model, this article proposes an inference recognition model that combines the convolutional features and the Bayesian network. The proposed model exploits the convolutional neural network to extract the detailed feature information of the object in the infrared image. Then, it uses the 2DPCANet screening mechanism to reduce the dimensionality and select more obvious detailed information in the candidate region. Based on the combination of human prior knowledge and posterior data, an infrared object recognition probability model is established through the application of the Bayesian network. The performance of the proposed algorithm is tested on the complex air combat simulation image dataset. The experimental results indicate that the recognition rate of the proposed anti-interference recognition algorithm reaches 94.17%.

An improved SSD method for infrared target detection based on convolutional neural network

Target detection is the basis for automatic target recognition system of infrared imaging guidance to complete subsequent tasks such as recognition and tracking. Existing systems have not the autonomous learning ability of target feature, and it will be powerless once the task environment exceeds the pre-planned condition. The single-stage target detection based on deep learning has the ability of autonomous learning and high computational efficiency, which is an effective way to solve the problem of infrared imaging guidance target detection in complex environment. SSD (Single Shot MultiBox Detector) is a classical single-stage detection model, however, the convolution layer with strong semantic information in SSD has low resolution, which is not conducive to small target detection. In addition, the location loss of SSD does not consider the impact of target scale change. Therefore, this paper puts forward two improvement ideas in view of SSD: (1) Starting from FPN (Feature Pyramid Network), feature channel’s importance is distinguished through efficient channel attention mechanism, the contribution of each feature layer to the fusion output is described based on the learnable weight, and the feature weighted fusion of bidirectional multi-scale is realized between the feature layer which has low resolution and strong semantics and the feature layer which has high resolution and weak semantics. (2) Starting from IoU (Intersection over Union) and considering non overlapping parts and geometric relationship between the predicted box and the ground-truth box, the location loss of SSD that remains invariable to the target scale change is constructed to improve the sensitivity of the detection model to the locating error of small target. The experimental results show that, for 300 × 300 input, the presented method achieves 84.7% mAP (mean Average Precision) on VOC2007 test and for 512 × 512 input, it reaches 86.6%. On the self-built infrared aircraft data set, the proposed method achieves 81.1% mAP and can detect more small targets. Without affecting detection speed, the presented method on experimental results outperforms some comparable state-of-the-art models such as YOLOv3 (You Only Look Once), DSSD (Deconvolutional Single Shot Multibox Detector), RSSD (Rainbow Single Shot Multibox Detector) and FSSD (Fusion Single Shot Multibox Detector).

Influence of jet components on the aero-optical effects of a 3D supersonic mixing layer.

Aero-optical effects in the mixing layer have caused significant concern due to the negative influence of high-speed vehicles with infrared imaging guidance systems. Here, we study the influence of different cooling mediums on the aero-optical effects. Four different cooling components are considered: helium (He), nitrogen (N2), air, and argon (Ar). The large eddy simulation method and ray-tracing method are used, respectively, to simulate the 3D mixing layer and to calculate the optical path difference (OPD). The numerical results show that, as the molecular weight of the cooling mediums increases, the mixing layer transition advances and the 3D effect of the flow field is enhanced. The local minimum OPD values of the wavefront distortion are significantly correlated with the large-scale vortex structure of the mixing layer. The compression effect plays a dominant role in aero-optics before the large-scale structure forms. Once the large-scale vortex structure generates, the aero-optical effect is conducted by the density and compression effect. The cooling medium helium delays the development of the mixing layer and subsequently reduces the aero-optical effects. However, once the large-scale vortex structure forms, the aero-optical effect becomes serious due to the largest density difference between air and He.

Aircraft tracking in infrared imagery with adaptive learning and interference suppression

Airborne target tracking is a crucial part of infrared imaging guidance. In contrast to visual tracking tasks, the target in infrared imagery shows different visual patterns. Moreover, severe background clutter and frequent occlusion caused by infrared interference make it a challenging task. Recently, discriminative correlation filter (DCF)-based trackers have shown impressive performance. However, the features adopted in DCF-based trackers are either handcrafted or pre-trained from a different task, which do not closely intertwine with the domain-specific video. To settle this problem, it is proposed to make full use of online training to learn domain-specific features. By integrating the correlation filter layer into the convolutional neural networks, the feature domain and the response maps of the DCF can be optimized iteratively in the initial frame. Meanwhile, utilizing the measurement of the response maps' peak strength, further adjustments to the feature domain can be made to achieve a sharper peak and suppress the interference region during the tracking process. Evaluations are conducted to prove the validity of proposed aircraft-tracking algorithm.

Joint feature embedding learning and correlation filters for aircraft tracking with infrared imagery

Infrared object tracking is a key technology for infrared imaging guidance. Blurred imaging, strong ego-motion and frequent occlusion make it difficult to maintain robust tracking. We observe that the features trained on ImageNet are not suitable for aircraft tracking with infrared imagery. In addition, for deep feature-based tracking, the main computational burden comes from the feedforward pass through the pretrained deep network. To this end, we present an airborne infrared target tracking algorithm that employs feature embedding learning and correlation filters to obtain improved performance. We develop a shallow network and a contrastive center loss function to learn the prototypical representation of the aircraft in the embedding space. The feature embedding module is lightweight and integrated into the efficient convolution operator framework for aircraft tracking. Finally, to demonstrate the effectiveness of our tracking algorithm, we conduct extensive experiments on airborne infrared imagery and benchmark trackers.

Research on the Guidance Technology of Air-To-Ship Missile Attacking Nearshore Ship

In the complex nearshore context, attacking warship has been one of the important combat tasks to air-to-ship missile. Due to the island environment and anti-air facilities, hitting effect significantly decreases which calls for higher requirements for guidance technology. This paper hackles the development of air-to-ship missile used to attack nearshore warship, expounds vital guidance technology like the TV guidance technique, the man-in-loop infrared imaging guidance technology, multi-mode compound guidance, etc., and analyzes the trend of the guidance technology of anti-aircraft missile for nearshore combat, which can provide valuable technical reference for study of new type of air-to-ship missile and the nearshore combat.

I-GANs for Infrared Image Generation

The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming. In order to address this problem, an infrared image generation method, infrared generative adversarial networks (I-GANs), based on conditional generative adversarial networks (CGAN) architecture is proposed. In I-GANs, visible images instead of random noise are used as the inputs, and the D-LinkNet network is also utilized to build the generative model, enabling improved learning of rich image textures and identification of dependencies between images. Moreover, the PatchGAN architecture is employed to build a discriminant model to process the high-frequency components of the images effectively and reduce the amount of calculation required. In addition, batch normalization is used to optimize the training process, and thereby, the instability and mode collapse of the generated adversarial network training can be alleviated. Finally, experimental verification is conducted on the produced infrared/visible light dataset (IVFG). The experimental results reveal that high-quality and reliable infrared data are generated by the proposed I-GANs.

Infrared Target Tracking Based on Improved Particle Filtering

Infrared target tracking technology is one of the core technologies in infrared imaging guidance systems and is also a hot research topic. The problem of particle degradation could be always found in traditional particle filtering, and a large number of particles are additionally required for accurate estimation. It is difficult to meet the requirements of a modern infrared imaging guidance system for accurate target tracking. To solve the problem of particle degradation and improve the performance of infrared target tracking, the extended Kalman filter and genetic algorithm are introduced into particle filtering, and an improved algorithm for infrared target tracking is proposed in this paper. In the framework of a particle filter algorithm, the Gaussian distribution for each particle is generated and propagated by a separate extended Kalman filter to improve the sampling accuracy and effectiveness of the probability density function of particles. Genetic algorithm is used to perform a resampling process to solve particle degradation and ensure the diversity of particle states in particle swarm. Simulation results show that the improved tracking algorithm based on improved particle filtering proposed in this paper can effectively solve the phenomenon of particle degradation and track the infrared target.

Infrared Imaging Guidance Semi-physical Simulation,Installation Error Analysis,and Correction

Infrared Imaging Guidance Semi-physical Simulation,Installation Error Analysis,and Correction

Infrared Dim and Small Target Detection Based on Denoising Autoencoder Network

The method of infrared small target detection is a crucial technology for infrared early-warning tasks, infrared imaging guidance, and large field of view target monitoring, and it is very important for certain early-warning tasks. In this paper, we propose an end-to-end infrared small target detection model (called CDAE) based on denoising autoencoder network and convolutional neural network, which treats small targets as “noise” in infrared images and transforms small target detection tasks into denoising problems. In addition, we use the perceptual loss to solve the problem of background texture feature loss in the encoding process, and propose the structural loss to make up for the perceptual loss defect in which small targets appear. We compare ten methods on six sequences and one single-frame dataset. Experimental results show that our method obtains the highest SCRG value on four sequences and the highest BSF value on six sequences. From the ROC curve, we can see that our method achieves the best results in all test sets.

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.

An improved weak light detector used for infrared Imaging guidance system

In infrared imaging guidance system, target won’t be detected successfully if the target is distant from the infrared detector or the light intensity of the target is very weak. Aimed to this problem, weak light detector is specially researched in this paper. A novel structure based on resonant tunneling diode (RTD) is proposed as a weak light detector. A multiplication is utilized to enhance the light sensitivity of the detector. Based on the Non-equilibrium Green's Function (NEGF) framework, the current – voltage (I – V) characteristics of the detector are simulated. The simulation results show that the detector has obvious light response and the light sensitivity is significantly enhanced via adding a multiplication.

Single Port Sentinel Lymph Node Biopsy in Endometrial Cancer

Endometrial cancer (EC) is the most common gynecologic malignancy. Initial therapy includes total hysterectomy plus or minus lymphadenectomy. Sentinel lymph node (SLN) biopsy has been proposed as a way to improve detection rates of EC and minimize morbidity of lymphadenectomy. Single institution data suggest that the SLN can accurately predict overall lymph node status in 92% of cases when used as part of a comprehensive protocol. Single port laparoscopy has been described for use in EC but has not been widely adopted despite possible advantages. Robotic surgery can potentially overcome the counterintuitive and opposite hand-instrument movements that have contributed to a steep learning curve in laparoscopic single site surgery. We present the first case of robotic single site hysterectomy with SLN biopsy and near infrared imaging guidance performed in EC in an effort to demonstrate that lymph node sampling is feasible with this platform.

Study on Dynamic IR Image Generation of Space Target

Dynamic IR image generation of space target is one of the key technologies in hardware in the loop simulation for the infrared imaging guidance system. The three-dimensional entity model is created in the Creator, Sinda/Fluint is used to analyze each part of dynamic infrared radiation characteristics from on-orbit Space Target, on the basis of the LRS infrared star catalogues, celestial background modeling is built. In Vega, the dynamic IR image of space target is generated. The simulation results show that the dynamic IR image of Space Target provide the important objective basis for the hardware in the loop simulation for the infrared imaging guidance system.

A Survey on Infrared Weak Small Target Detection Method

Infrared weak small target detection is one of the key technologies in the early infrared imaging guidance and wide-field view surveillance system. In the complex and low signal-to-noise ratio background, the target has only a few pixels. There is no shape and texture information to use. It brings great difficulties to the infrared weak small target detection. In this paper, we sum up the research status of infrared weak small target detection method, and analyze the key problems of infrared weak small targets detection.

Portable robot for autonomous venipuncture using 3D near infrared image guidance.

Venipuncture is pivotal to a wide range of clinical interventions and is consequently the leading cause of medical injury in the U.S. Complications associated with venipuncture are exacerbated in difficult settings, where the rate of success depends heavily on the patient's physiology and the practitioner's experience. In this paper, we describe a device that improves the accuracy and safety of the procedure by autonomously establishing a peripheral line for blood draws and IV's. The device combines a near-infrared imaging system, computer vision software, and a robotically driven needle within a portable shell. The device operates by imaging and mapping in real-time the 3D spatial coordinates of subcutaneous veins in order to direct the needle into a designated vein. We demonstrate proof of concept by assessing imaging performance in humans and cannulation accuracy on an advanced phlebotomy training model.

Effect Evaluation Research on Laser Disturbance to Infrared Imaging Guidance Sensor System

Effect Evaluation Research on Laser Disturbance to Infrared Imaging Guidance Sensor System

Dynamic infrared scene simulation using grayscale modulation of digital micro-mirror device

Dynamic infrared scene simulation is for discovering and solving the problems encountered in designing, developing and manufacturing infrared imaging guidance weapons. The infrared scene simulation is explored by using the digital grayscale modulation method. The infrared image modulation model of a digital micro-mirror device (DMD) is established and then the infrared scene simulator prototype which is based on DMD grayscale modulation is developed. To evaluate its main parameters such as resolution, contrast, minimum temperature difference, gray scale, various DMD subsystems such as signal decoding, image normalization, synchronization drive, pulse width modulation (PWM) and DMD chips are designed. The infrared scene simulator is tested on a certain infrared missile seeker. The test results show preliminarily that the infrared scene simulator has high gray scale, small geometrical distortion and highly resolvable imaging resolution and contrast and yields high-fidelity images, thus being able to meet the requirements for the infrared scene simulation inside a laboratory.

2051 POSTER Feasibility of Frameless Cranial Radiosurgery With Infrared Markers Fixed to an Immobilization Mask That is Monitored Under X-ray and Infrared Image Guidance System

2051 POSTER Feasibility of Frameless Cranial Radiosurgery With Infrared Markers Fixed to an Immobilization Mask That is Monitored Under X-ray and Infrared Image Guidance System