Unlike the visible light auto-focusing system, the infrared auto-focusing system is divided into far-to-near focusing and near-to-far focusing owing to the special imaging principle of the infrared detector. The auto-focusing functions in the two processes are based on the analysis of the characteristics of the respective focusing function curves. To this end, five targeted evaluation indexes are used: sensitivity, the width of the steep part of the focusing curve, steepness, variance of the flat part of the focusing curve, and time. The 13 typical sharpness evaluation functions that are commonly used in quantitative analysis are conducted, and an optimal function suitable for the two focusing processes is proposed. The results show that FLaplace can be used as the optimal function in the focusing process from near to far, and FLaplace and FSML can be used as the optimal function in focusing from near to far.
As field-programmable gate arrays(FPGAs) become increasingly used in large-scale systems, it is often difficult for a single-chip FPGA to perform all the tasks required. High-speed and stable communication between multiple FPGAs has become a focus of research in this field. For this purpose, a verification protocol based on low-voltage differential signaling (LVDS) that can be used for high-speed and stable communication between FPGA chips was designed. This protocol performs multiple rounds of multipath verification based on conventional LVDS communication to improve transmission reliability. Based on this protocol, a nine-channel LVDS communication test system consisting of two Xilinx 7 series FPGAs was built. One channel was used to synchronize the clock, and the other eight channels were used for checksum communication. After a long period of high- and low-temperature tests, the bit error rate was greatly reduced compared with conventional LVDS communications while ensuring a single transmission rate of 1.2 Gb/s.
The registration accuracy of image fusion is an important performance index that is related to the quality of image fusion. The infrared and visible image fusion objective optical system in this study adopts a parallel optical path layout and an optical passive thermal compensation method to improve the registration accuracy of image fusion. First, the contributions of mechanical thermal compensation and optical thermal compensation are analyzed and compared to improve the image registration accuracy. Second, according to the performance index of the image fusion objective system, the optical passive thermal compensation designs of the infrared objective and visible objective are optimized. Moreover, the necessity of optical passive thermal compensation design of the visible objective is analyzed. Finally, according to the imaging quality and image registration effect of the image fusion objective system, it is concluded that the quality of the fused image is high and that the requirements of the index can be achieved.
In the focusing system design of an infrared(IR) imager, the motor drives the focusing lens group to reciprocate along the linear guide rail to change the position of the focusing lens group accurately and to change the focal length. To control the position of the focus lens group accurately, the focus control system requires a high-resolution encoder to reflect the position of the focus lens group to realize closed-loop control of the group. In the focusing control system of the IR imager, an incremental encoder is used to feedback the position of the focusing lens group. According to the characteristics of the incremental encoder, because of the rich logic resources and programmable flexibility of the complex programmable logic device, a readout circuit is designed that can accurately feedback the position of the focusing lens group in real time. Verification in an actual project demonstrated that this scheme can read out the position of the incremental encoder accurately in real time, has reliable anti-interference ability, and meets the system requirements of high-precision position control.
To make the fusion image show more image details and to obtain a better image visual effect, a fusion method based on residual significance is proposed. First, the infrared image is analyzed using residual significance to obtain its significance coefficients. Then, the source images are decomposed using a dual-tree complex wavelet transform, and the low- and high-frequency components are fused according to different fusion rules. Finally, the fusion image is reconstructed using the inverse transformation of a dual-tree complex wavelet. Experimental results showed that the fusion method proposed in this paper produced higher quality images and better visual effects than those of the traditional fusion method.
Sea-skyline extraction is an important research subject in the development of infrared search-and-rescue equipment for offshore target detection and tracking. This study investigates sea-sky background target-extraction technology and develops a practical sea–skyline extraction algorithm. First, this work uses spatial filtering to eliminate the interference of a small-vessel target and near-domain sea clutter; it then performs a morphological gradient operation, obtains the edge contour of the sea–skyline using the Ostu threshold-segmentation method, obtains a point group using the Hough transform, and extracts the sea-skyline using the least square method. The experimental results show that the algorithm can accurately and quickly extract the sea-skyline. This study provides a foundation for the rapid detection and tracking of offshore targets.
Rotating machinery is the core component of mechanical equipment and can thus cause a significant loss if it breaks down. Therefore, real-time monitoring and diagnosis of the rotating machinery is critical. Automated infrared intelligent monitoring and diagnosis is a recent development in fault diagnosis. To realize infrared intelligent monitoring and diagnosis, it is necessary to accurately identify rotating machinery components. In this study, an infrared thermal camera was used to monitor the running state of the rotating machinery and infrared images of the motor, coupling, bearing seat, gearbox, and other equipment. The Faster R-CNN algorithm was used to train the rotating-machinery infrared images and to identify the targets. The results showed that the algorithm can accurately identify rotating machinery components. The recognition effect of single-angle and rotating-angle infrared monitoring was studied. It was found that the detection effect of infrared gray images fortraining at the same angle is better than that of infrared pseudo-color images. The influence of four types of pre-training networks on infrared target recognition was compared. The average detection accuracy of the resnet50 pre-training network was 0.9345, and the recognition accuracy was higher.
Aiming at the problems of low target contrast and insufficiently clear images in the process of visual saliency fusion, this paper proposes an improved frequency Tuned algorithm based on bi-dimensional empirical mode decomposition (BEMD). First, the strong points and contour information of the infrared image captured by BEMD is used to guide the generation of saliency maps of the infrared image. Then, the visible image and the enhanced infrared image are subjected to a non-subsampled contourlet transform(NSCT). The saliency map-guided fusion rule is used for the low-frequency part. The high-frequency part is used to set the area energy to be large and rely on the threshold value rules. Finally, the inverse NSCT transform is used to generate a fused image and subjective visual and objective index evaluations are performed to it. The results show that the method in this paper achieves a multi-level and adaptive analysis of the original image, and achieves good vision compared to the contrast methods.
Image enhancement can be divided into two kinds: global enhancement and local enhancement. Current image enhancement techniques based on local enhancement cannot accurately segment the target area and background, and it is difficult to enhance the segmentation region adaptively. In this paper, a region-adaptive multi-scale strong light fusion algorithm is proposed for infrared image enhancement. Firstly, semantic segmentation technology is used to divide the target area and background area. Then, the improved multi-scale strong light fusion algorithm is used to enhance each area adaptively. The experimental results show that the enhancement effect of the proposed algorithm is better than that of the current conventional algorithms, and the visual effect of image enhancement is more realistic.
In this study, a semiconductor device was tested for high temperature storage at 90℃, 80℃ and 70℃, and the failure data is obtained. Based on the Weibull distribution model, parameter estimation was carried out by the least square method. The failure distribution function of the semiconductor device was obtained. And the classical reliability theory was applied to calculate the characteristic life, reliable life and MTBF of the product at 90℃, 80℃ and 70℃. Using the Arrhenius model, the storage characteristic life of the semiconductor device at room temperature was obtained, according to the storage characteristic life of 90℃, 80℃ and 70℃. The results show that the method is reasonable, simple and effective, and the results can be used to derive the normal temperature storage life.
A hyperspectral infrared focal plane complementary metal–oxide semiconductor (CMOS) readout integrated circuit (ROIC) was developed for satellite applications. The ROIC design includes row and gain selection functions for each line to meet the new requirements of hyperspectral applications in ROICs. Further, the ROIC optionally supports 7-gain features and is suited for medium and shortwave MCT chips; other features of the proposed design include integration while reading, anti-blooming, series port control, and full-chip current injection test functions. The proposed ROIC was fabricated in a 0.35 μm stitching process with a 5 V power supply; the test results show good performance of the ROIC, with a full-frame rate of 450 Hz and adjustable power dissipation having a typical value of 300 mW. This paper introduces the basic structure of the readout circuit design, shows the problems in the design and the corresponding solutions, and gives the test results of the circuit at the end of the paper.
The traditional index designed to evaluate infrared anti-jamming ability is relatively simpleand is mostly determined by the comprehensive anti-jamming probability. To solve this problem, we establishan anti-jamming performance evaluation index system including three levels of missiles, guidance systems, and seekers; construct the anti-jamming performance evaluation function; decompose anti-jamming performance evaluation indexes; and improve the anti-jamming performance of the guidance system and seeker. The evaluation ability of the system improves the ability touse the evaluation results of the seeker and guidance system to predict the anti-jamming performance of the guidance system.
To support the infrared air–air–missile development of medium and long ranges, an estimation method for the infrared seeker detection range is proposed. Lock-on-after-launch technology is employed. It is based on an “information binding and scene model” . The method is based on the classical theory of infrared system performance estimation. Through the analysis of the environmental characteristics of the air battlefield, the information binding requirement from the aircraft or control system is completed. An estimate scheme block diagram and an example for engineering applications are given. The results show that the method can give distance estimation results that match the real-time situation, and it has the advantages of a small data scale, fast query speed, and minimal calculation steps. The method addresses real-time application needs under limited resources.
Existing methods for detecting the number of vehicle occupants in a high-occupancy vehicle (HOV) lane, using radar and infrared thermal imaging technology, exhibit low reliability and low accuracy. To address these limitations, a method for detecting the number of vehicle occupants based on multispectral infrared imaging and an improved Faster regions with convolutional neural networks (R-CNN) algorithm is proposed. The vehicle interior space image is obtained using a multispectral infrared imaging system, and the number of passengers is detected by a Faster R-CNN deep learning algorithm. The generalization ability of the network is enhanced using the full convolution network structure and multiscale feature prediction, and ROI-Align is used instead of ROI-Pooling. Through K-means clustering, the prior distribution of the geometric proportion of the length and width of the target frame is obtained, which improves the training speed and the accuracy of position regression of the region proposal network (RPN). The test results showed that the interior space image was clear, and the algorithm could detect the number of passengers. After its improvement, the generalization ability of the network was enhanced, and the accuracy of single occupant detection reached 88.6%, which was 13.8% higher than before its improvement. This meets the requirements of more than 80% of industry regulations.
It is difficult to directly distinguish the human body area identified by the infrared data measured by medical infrared thermal imaging equipment from the temperature data obtained by conversion. It is often necessary to convert it into image data and use image processing technology to obtain the region of interest and the biological characteristics from the temperature data in the given area. Accordingly, disease screening or diagnosis can be realized. However, conversion from 14-bit infrared data to 8-bit image data incurs a serious loss of data accuracy, resulting in poor processing performance. In this paper, a new expression method for thermal images is proposed. The obtained color thermal image contains the original precision temperature data information and the color enhancement effect under the setting scale of the temperature observation window. At the same time, it contains the temperature data record and the setting rules of the observation window. Through the inverse transformation of the image data, the original temperature data can be reproduced and the color enhancement effect can be changed. The thermal image provided by this method can be used in different infrared thermal image systems without requiring additional access to temperature data files. This will be more aligned with the development trend of big data and artificial intelligence.