Special issue-Infrared low light level night vision technology
2022, 51(2): 20220128.
doi: 10.3788/IRLA20220128
Night vision imaging technology converts invisible radiation or enhances faint light at night under low illumination conditions to enable human eyes to see covertly at night. It plays an important role in modern military applications such as night detection and targeting, assisited driving, navigation and guidance. In order to ensure "one-way transparency" and give full play to the technical advantages of "dominating the night", the world's military powers have invested a lot of human and material resources to carry out research on advanced night vision imaging technology, so that the performance of night vision equipment can be rapidly developed. As the first article of this special issue of "Nanjing University of Science and Technology" for the Journal of Infrared and Laser Engineering, this paper outlines the current progress and challenges of night vision imaging technology, and provides a discussion and outlook of the future development trend of advanced night vision imaging technology——direct imaging based on photoelectric conversion and constructive imaging based on computational imaging, respectively.
2022, 51(2): 20210901.
doi: 10.3788/IRLA20210901
A dynamic simulation method for moving target recognition tracking and trajectory reconstruction in infrared thermal imaging video was proposed. Through the generation of virtual infrared images in the simulation environment and the basic model of imaging, a series of preprocessing was performed on the obtained images. A dynamic simulation platform based on Gazebo and OpenCV was built in the air-to-air scene. The smooth constraint algorithm was used to reconstruct the real-time dynamic trajectory of the tracking target. The error analysis model was proposed, and the performance of the trajectory reconstruction algorithm and the effectiveness of the simulation platform were analyzed. The experimental results show that this method has good accuracy and robustness for the trajectory reconstruction of infrared moving target in air-to-air scenario, and basically has no constraint on the motion model of the target. At the same time, the simulation platform has high operation efficiency and real-time performance. The real-time dynamic simulation above 60 fps can be realized by the ordinary household computer, which meets the performance test and training requirements of the trajectory reconstruction algorithm. The core algorithm can also be migrated to the airborne computing platform to realize the real-time trajectory reconstruction in the real scene. The proposed dynamic simulation method of moving target trajectory reconstruction in single-channel thermal imaging video is of great significance to the research of three-dimensional trajectory reconstruction and dynamic ranging and positioning of space targets.
2022, 51(2): 20210896.
doi: 10.3788/IRLA20210896
Simulation of earth’s limb scene plays a key role in satellite infrared detection field. It is an important basis for long-range detection of high-speed airborne targets. In limb detection, the traditional infrared ocean simulation method based on three-dimensional ocean appearance and the calculation of radiation characteristics is not applicable, because the earth surface approximates a sphere. Also, the thickness and height of clouds have important influence on the calculation of infrared radiative transmission characteristics, where the method of considering the cloud as particle cluster would greatly reduce the speed of simulation. Therefore, the infrared remote sensing imaging simulation method for earth’s limb scene was established by conducting the infrared radiation model of ocean and cloud, the transformation relationship between earth-space coordinate system and infrared camera coordinate system, and the atmospheric transmission model. According to the components of scene, the ocean distribution model and multi-layer clouds distribution model were established respectively, and the infrared radiation model of the earth’s limb scene was established according to the infrared radiation and reflection characteristics of ocean and clouds. The infrared remote sensing simulation images of the earth’s limb scene under various observation angles were calculated by the conversion relationship between earth-space coordinate system and camera coordinate system, the theory of atmospheric transmission and the sensor effect. The simulation results show that the infrared image accord with the infrared radiation characteristics of earth’s limb scene. The average Laplacian sum of simulation images is 0.15, and the grayscale gradient average value of the images is 0.70.
2022, 51(2): 20210844.
doi: 10.3788/IRLA20210844
In infrared remote sensing images with low or medium spatial resolution, the number of pixels occupied by ships on the sea is very small, and the geometric shape and specific texture structure of the target are difficult to obtain. In order to improve the detection limit signal to clutter ratio, the ship wake feature with linear feature was taken as the detection element, which was mathematically characterized. The Dot-Curve detection system was established innovatively. Based on the two-dimensional curvature filtering, the ship detection and wake feature extraction were carried out preliminarily. The feature set was established, from which a number of features with large difference from the background interference items, including wake gray variance, positive and negative gray slope on both sides of the wake, wake linearity and the distance from the hull detection results, were selected to identify the detection results of the candidate targets, remove interference items and extract targets. The results show that after target identification, the ship false detection rate in different bands of infrared images is reduced to less than 8.40%, and the detection rate is improved to at least 94.53%. The ship detection algorithm combines the physical and image characteristics of the wake, which is suitable for many scenes and bands. The algorithm is refined and effective, the physical laws are clear, and the samples needed are few.
2022, 51(2): 20210885.
doi: 10.3788/IRLA20210885
Single-photon lidar has been widely used to obtain depth and intensity information of a three-dimensional scene. For multi-surface targets, such as when the laser transmit through a translucent surface, the echo signal detected on one pixel may contain multiple peaks. Traditional methods cannot accurately estimate multi-depth images under low photon or relatively high background noise levels. Therefore, a time-correlated multi-depth estimation method was introduced. Based on the time correlation of the signal responses, a multi-depth fast denoising method was adopted to point cloud data, and could identify the signal responses of multiple surfaces from background noise on each pixel. Considering the Poisson distribution model of the signal response set, the spatial correlation between pixels was introduced through total variation (TV) regularization to establish a multi-depth estimation cost function. The fast-converging alternating direction method of multipliers (ADMM) was used to estimate the depth image from the cost function. Experimental results on a multi-depth target at a distance of about 1 km show that the root mean square error (RMSE) and signal to reconstruction-error ratio (SRE) of the depth image estimated by the proposed method can be at least 27.05% and 18.39% better than that of other state-of-the-art methods. In addition, the data volume of this method is reduced to 4% of the original. It is proved that this method can effectively improve the multi-depth image estimation of single-photon lidar with smaller memory requirements and computational complexity.
2022, 51(2): 20210871.
doi: 10.3788/IRLA20210871
LiDAR systems with active illumination obtain depth information of the scene using Single-Photon Avalanche Diode(SPAD) detectors to record the arrival time of reflected photons from the laser pulse. However, there is ambient light that interferes measurements during the detection period. Sensor fusion is one of the effective methods for single-photon imaging. Recently, many data-driven methods based on intensity-LiDAR fusion have achieved gratifying results, but most of them use the scanning LiDAR which has a slow depth acquisition speed. The advent of the SPAD array can overcome the limitation of frame rates. The SPAD array allows the collection of multiple returned photons at the same time, which accelerates the information collection process. However, the spatial resolution of SPAD array detectors is typically low, and the detection process is also interfered by the ambient light. Therefore, it is necessary to break the inherent limitation of the SPAD array through an algorithm to separate the depth information from the noise. In this paper, for the SPAD array detector with the array size of 32×32 pixel, a convolutional neural network was proposed, which could reconstruct high-resolution clean TCSPC histogram under the guidance of the intensity image. A multi-scale approach was adopted to extract input features, and the fusion of depth data and intensity data was further processed based on the attention mechanism in the network. In addition, a loss function combination suitable for the TCSPC histogram data processing network was designed, where the overall distribution of photons and the ordinal relationship between time bins in the temporal dimension could be simultaneously considered. The method proposed in this paper can successfully increase the depth spatial resolution by 4 times, and the efficacy of proposed method is verified on realistic data, which is superior to state-of-the-art methods qualitatively and quantitatively.
2022, 51(2): 20210864.
doi: 10.3788/IRLA20210864
As one of the important directions of computer vision, target tracking has a wide range of applications, such as autopilot, UAV tracking, but the target tracking algorithm cannot run effectively on embedded devices. A novel acceleration target tracking scheme based on correlation filtering was proposed to solve the problems of target tracking algorithm, such as high computation and complexity, difficulty application on the resource-constrained embedded devices. Firstly, the adaptive fuzzy algorithm was used to optimize the overall computation of the algorithm, which could decide whether to reduce the image quality based on target size. Secondly, the criterion of Peak-to-Sidelobe Rate and Average Peak-to-Correlation Energy were used to measure the reliability of tracking results, so as to realize adaptive updating of tracking model and re-search of target location. Finally, for the correlation operation and complex matrix multiplication operation in the stage of training tracking detector, which were implemented based on FPGA parallelly to improve the real-time energy efficiency of the algorithm. The proposed acceleration algorithm was deployed on PYNQ-Z2 and verified based on OTB-2015 tracking data set. The tracking accuracy and real-time performance of the algorithm were 65.8% and 17.28 frame/s, respectively, compared with the original algorithm, the tracking accuracy and real-time performance were improved by 9.12% and 703.7%, respectively.
2022, 51(2): 20210455.
doi: 10.3788/IRLA20210455
An infrared/passive millimeter wave compound decoy was prepared. On the basis of MTV pyrotechnic composition, red phosphorus was used to replace part of magnesium powder, short carbon fiber was used as a functional additive, and a thin-film pyrotechnic material was prepared. The infrared radiation and millimeter wave radiation properties of this material were tested and analyzed. The research results show that the addition of a small amount of red phosphorus was beneficial to increase the radiation area; When the proportion of red phosphorus added is more than 10%, the average flame temperature, infrared radiation intensity and millimeter wave brightness temperature continue to decrease with the increase of red phosphorus content, after adding a suitable amount of carbon fiber, the burning rate, flame temperature and the infrared radiation are both enhanced; The millimeter wave brightness temperature continues to increase with the increase of carbon fiber content, when the amount of red phosphorus added is 10% and the carbon fiber content increases from 0 to 1.75%, the millimeter wave brightness temperature increased from 330 K to 458 K, brightness temperature greater than 400 K, longer duration.
