With the development of ultraviolet detection technology, oxide materials showing the unique advantages in the field of ultraviolet detection, which the traditional detectors didn’t possess, and becoming a hot research topic in recent years. It is a fast-developing dual-purpose detection technology after the infrared detection technology. However, the wide applications of oxide-based ultraviolet detectors still face challenges. In this paper, we have summarized the applications and development histories of the ultraviolet detection technology at home and abroad. The crystal structures, properties and progresses in devices of three kinds of metal oxide ultraviolet materials are summarized and discussed. In the end, the problems in the research of the oxide-based ultraviolet detection materials and devices are analyzed, and the development of the oxide-based ultraviolet detection technology is summarized and prospected.
At present, the technology for the performance evaluation of gas leak thermal imaging detectors is not sufficiently well-developed, and there is no systematic research report on the test systems or methods of the corresponding evaluation index. However, it is difficult for the performance evaluation methods of conventional thermal imaging cameras to directly evaluate the detection capabilities of gas leak thermal imaging systems. We designed a test and evaluation system that can measure the performance of gas leak thermal imaging systems with multiple types of performance indicators. NECL, MRGC, and MDGC were measured experimentally in a laboratory environment with ethylene and methane gas as the detection target. The experimental measurements verify that the test evaluation system is feasible and practical.
Thermal load is one of the main reasons for the failure of infrared detection system, therefore, thermal-structure coupling analysis of a certain type of infrared imaging optical detection module under different temperature loads by means of ANSYS Workbench software was performed in this study. First, the response of the back intercept between the optical lens and the detector under different temperature loads was observed, and then, the theoretical imaging quality was calculated by the optical software ZEMAX on the basis of the back intercept. Finally, the theoretical calculation models were verified by the environment test. Simultaneously, the deformation rule of the optical detection module under different temperature loads was obtained, and it was found that the conductivity coefficient and thermal expansivity of the installation material of the detector affected the stability of the detection module. This research can provide guidance on the design, optimization, and reliability of infrared imaging optical detection modules.
Airborne infrared measurement systems are mounted on aircraft to track and measure the infrared characteristics of the ground target. The impact of platform jitter and environmental change must be addressed to achieve stable tracking and measurement accuracy. To this end, the technology of passive isolation and active gyroscopic stability control was used in this study to examine the influence of airborne platform jitter. To ensure stable tracking and imaging, the high-frequency disturbances were isolated through a passive shock absorber, and the low-frequency disturbances were suppressed through a frame servo system. Non-thermalization compensation calculation and focusing compensation control were adopted in the design of the optical lens to ensure clear imaging of the equipment and thus study the influence of the temperature change of the ground and air environment on the infrared optical system. Simulation calculations and field measurement demonstrated that these vibration reduction measures are reasonable and effective. The equipment thus developed is stable in tracking and clear in imaging. Therefore, it is suitable for realizing hanging flight measurement on different platforms.
A compact thermal imaging system, operating over both near and far distances, for armed police is introduced. A design example is proposed. The working band is 8–12 .m, the center wavelength is 10 .m, the long focal length is 150 mm, the short focal length is 50 mm, and the F number is 1.1. The system can match the performance of an uncooled focal plane infrared detector with a pixel number of 384.288 and a pixel size of 17 .m.17 .m. The system realizes zooming by the axial movement of the zoom group. By using binary optical surface and aspheric surface, the volume and mass of the imaging system are reduced, and the image quality is improved. The design results show that the thermal imaging system has appropriate image quality, which is suitable for fire rescue, fire prevention, night detection, and border monitoring.
We design and develop a Cassegrain off-axis reflection system collimator with a focal length of 8 m according to production requirements and construct an advanced installation and adjustment method to accurately calibrate it. Through the interpretation of images and interference fringes, the imaging quality of the collimator system is found to be close to the designed value. We solve the problem of the infrared discrimination test target board in production because the longest focal length of the existing collimator focal length is 3 m, and there is no corresponding spatial frequency required by the product.
In view of the limitations of traditional detection methods that can not obtain the polarization information and spectral information of camouflage materials in complex background, this paper uses hyperspectral polarization imaging technology to detect and analyze the high-spectral polarization characteristics of typical camouflage materials. In the experiment, a set of simultaneous aperture hyperspectral polarization imaging system was used to detect the hyperspectral polarization of typical camouflage materials such as camouflage coating and camouflage net, and to analyze the nine polarization parameters of the camouflage material, and obtain the relative reflectance of camouflage material and background with spectral variation. The results show that by using the difference of polarization characteristics between the camouflage material and the background, selecting the appropriate polarization parameter can increase the texture detail of the target and improve its contrast. The spectral polarization characteristics are analyzed, and the 760 nm detection band is selected to facilitate the rapid and accurate detection of the camouflage material.
A roll surface in aluminum processing is smooth, has strong reflective characteristics, and is easily affected by ambient light when using an infrared temperature sensor to measure the temperature, resulting in low temperature measurement accuracy on the roll surface. The cooling control system affects the precision of cooling treatment on the roll surface, resulting in poor product quality. In this study, an infrared temperature compensation algorithm based on light intensity is proposed and constructed to improve the accuracy of ambient light measurement of the surface temperature of a strong reflector. Experimental results show that this method can compensate for measurement errors caused by changes in illumination intensity, thereby improving the measurement accuracy. The algorithm is simple and adaptable and provides a new approach to strengthening the accuracy of temperature measurement given speed change.
To reduce the redundancy in hyperspectral images and further explore their potential classification information, a convolutional neural network(CNN) classification model based on feature importance is proposed. First, the random forest(RF) model obtained by Bayesian optimization training is used to evaluate the importance of hyperspectral images. Second, an appropriate number of hyperspectral image bands are selected as new training samples according to the evaluation results. Finally, the 3D-CNN is used to extract and classify the obtained samples. Based on two sets of measured hyperspectral remote sensing image data, the experimental results demonstrate the following: compared with the original spectral information obtained directly using a support vector machine(SVM) and the CNN classification effect, the proposed hyperspectral classification model based on feature importance can effectively improve the classification accuracy of hyperspectral images while reducing dimensionality.（cstc2014yykfB30003）。
To accelerate the simulation speed and improve the coverage of verification for a field programmable gate array (FPGA) implemented with dead pixel correction of an infrared image, an FPGA automatic verification platform based on SystemVerilog-Direct programming interface(SV-DPI) was designed. Using DPI programming interface technology, the C++ programming language was invoked by the SV platform. A generator and correction model for dead pixel data of infrared images was built. This established a communication between two languages on the transaction level. The results show that, compared with the traditional verification method, the proposed platform is simple in structure and can quickly generate a test vector, construct a reference model, and check results automatically. It realizes automated verification for an FPGA implemented with dead pixel detection and correction of an infrared image. The function coverage can reach 100%. It effectively shortens the period of construction and debugging for the FPGA verification platform and improves the efficiency and quality of verification.
The widely used infrared diagnosis of power grids is significantly influenced by the detection environment and professional level of personnels. The automation and intelligence level of conventional infrared thermal imagers are not sufficiently high. Therefore, this paper presents an intelligent infrared diagnosis system for power grid equipment, which includes an environment parameter module, ranging module, equipment type identification module, equipment material judgment module, radiation rate setting module, temperature measurement module, and report generation module. The system automatically detects the ambient temperature, humidity, wind speed, and detection distance with the equipment, as well as automatically identifies the equipment material type, determines the radiation rate, and automatically sets the aforementioned parameters in the thermal imager. The thermal imager judges the equipment type through image recognition, automatically reads the temperature data of the corresponding position of the equipment according to the judgment method and criterion of the infrared diagnosis standard of the equipment, and obtains the detection conclusion by calculation. It not only reduces the number of instruments required to be carried by the infrared detection personnel, but also realizes the automatic setting of instrument detection parameters, with intelligent identification of equipment types and automatic generation of detection conclusions, thereby reducing the level of professional requirements for detection personnel.
This study proposes and designs an automatic defect detection system based on UAV images for wind turbine blades, aimed at alleviating problems with manual detection methods, such as low efficiency and inaccurate defect detection. This paper introduces the system's image acquisition system, acquisition method, defect detection principle, and detection result. This system uses a UAV as the flying carrier to realize automatic inspection of wind turbine blades, thereby improving the inspection efficiency and reducing the manual workload. Through image segmentation and defect detection algorithm design, automatic detection of suspicious defect areas is achieved. Double light fusion of visible and infrared light improves the accuracy of automatic blade defect recognition. After multiple field tests and verification, the system is shown to accurately and quickly realize the automatic identification and detection of defects, such as bulges, cracks, and wrinkles.