For long-distance and wide field-of-view scenes, infrared target detection has significant challenges owing to the principle of a thermal imager, interference of the atmospheric environment, and attenuation of infrared radiation by long-distance transmission media. Based on the characteristic analysis of small-target infrared images, such as complex background, dim and small targets, low image contrast, and lack of image structures, we reviewed the research status of infrared dim small-target detection from target highlight and background estimation and discussed the development trend of infrared dim small-target detection.
Tandem white OLEDs offer low power consumption, high brightness, and a high color gamut. However, the material and electrical structures of tandem white OLEDs still need to be optimized owing to the outstanding challenges in efficiency, lifetime, and driving voltage. In this study, we focused on the latest research on tandem white OLEDs and summarized the problems in engineering preparation and non-destructive detection method of 3 types of CGLs for high-efficiency tandem white OLEDs. We focused on the latest research on the "all-phosphorescent system, " "harvesting excitons via two parallel channels, " and the "mixed-phosphorescent-TADF system" simultaneously. We summarized the device lifetime problems and discussed structural solutions such as "graded doping" and "four-color mixed-phosphorescent-TADF system." From the aspect of CGL materials and structures in different systems, we reviewed the scheme of lower driving voltage for tandem white OLEDs. Finally, we provided suggestions for improving the materials and structures of tandem white OLEDs.
We proposed a color-corrected underwater illumination image fusion method based on color correction to address uneven color shifts, low contrast, and blurred details in underwater illumination images. First, we exploited the pixel correlation between image channels to compensate for the red channel. Then, based on the color-corrected image, a sharpness-enhanced image is obtained using a nonlinear unsharp masking technique, and a global stretching map is obtained using a restricted histogram with Rayleigh distribution. Finally, we generated the fused image using a multi-scale fusion strategy. The experimental results on a self-built dataset (RULI) showed that the proposed method could remove the inhomogeneous scattering interference of mixed illumination in the imaging process and substantially improve the detail sharpness of the image. The mean values of the image quality assessment metrics UIQM and IE were 4.7399 and 7.7617, respectively, better than those of related algorithms in the existing literature.
Aiming at more redundant background information and low stitching accuracy of the infrared images of the blades taken by UAV (Unmanned Aerial Vehicle), In this study, we proposed a stitching algorithm for infrared wind turbine blade images combining the Chan-Vese model and morphology. First, we subjected the image to median filtering and noise reduction, and a morphological operation improved a level-set algorithm based on the Chan-Vese model to generate the mask of the expression subject. We extracted Harris feature points by removing redundant backgrounds based on the mask. We performed morphological etching on the mask to suppress the pseudo-feature points on the boundary-jagged pixels. We used violent matching and the RANSAC algorithm to screen out effective matching point pairs and calculate the homography matrix to realize matching and splicing. Compared with the Harris stitching algorithm under traditional image segmentation, the stitching accuracy of the improved algorithm significantly improved, and it showed strong robustness in different test scenarios.
Considering the false alarm and real-time requirements of infrared small-target detection under a complex cloud background, a novel algorithm is proposed based on structure tensor screening and local contrast analysis. Combined with the feature that the maximum eigenvalue of the structure tensor of the target area is larger than that of other background areas, the proposed algorithm can filter out most nontarget areas and retain a few suspicious areas. Local contrast calculation performed on suspicious areas can enhance the target, suppress the residual background, and effectively reduce computation. The algorithm steps are as follows: first, we constructed the structure tensor matrix within the local image area captured by the sliding window, and where the maximum eigenvalue is larger than the threshold is marked as a suspicious area. Then, we calculated the ratio-difference joint local contrast. Finally, we adopted an adaptive threshold segmentation on the saliency map to extract the real target. Experimental results showed that the proposed algorithm can achieve a higher detection rate, lower false alarm rate, and shorter running time under a complex cloud background.
We proposed a multimodal crowd counting algorithm based on RGB-Thermal (RGB-T) images (two-stream residual expansion network) in crowd counting, given scale changes, uneven pedestrian distribution, and poor imaging conditions at night. It has a front-end feature extraction network, multi-scale residual dilation convolution, and global attention modules. We used the front-end network to extract RGB and thermal features, and the dilated convolution module further extracted pedestrian feature information at different scales and used the global attention module to establish dependencies between global features. We also introduced a new multi-scale dissimilarity loss method to improve the counting performance of the network and conducted comparative experiments on the RGBT crowd counting (RGBT-CC) and DroneRGBT datasets to evaluate the method. Experimental results showed that compared with the cross-modal collaborative representation learning (CMCRL) algorithm on the RGBT-CC dataset, the grid average mean absolute error (GAME (0)) and root mean squared error (RMSE) of this algorithm are reduced by 0.8 and 3.49, respectively. On the DroneRGBT dataset, the algorithm are reduced by 0.34 and 0.17, respectively, compared to the multimodal crowd counting network (MMCCN) algorithm, indicating better counting performance.
We proposed a new object detection method based on the CSE-YOLOv5 (CBAM-SPPF-EIoU-YOLOv5) model for insufficient multi-scale feature learning ability and the difficulty of balancing detection accuracy and model parameter quantity in remote sensing image object detection algorithms in complex task scenarios. We built this method on the YOLOv5 model's backbone network framework and introduced a convolutional attention mechanism layer into the shallow layers to enhance the model's ability to extract refined features and suppress redundant information interference. In the deep layers, we constructed a spatial pyramid pooling fast (SPPF) with a tandem construction module and improved the statistical pooling method to fuse multi-scale key feature information from shallow to deep. In addition, we further enhanced the multi-scale feature learning ability by optimizing the anchor box mechanism and improving the loss function. The experimental results demonstrated the superior performance of the CSE-YOLOv5 series models on the publicly available datasets RSOD, DIOR, and DOTA. The average mean precisions (mAP@0.5) were 96.8%, 92.0%, and 71.0% for RSOD, DIOR, and DOTA, respectively. Furthermore, the average mAP@0.5:0.95 at a wider IoU range of 0.5 to 0.95 achieved 87.0%, 78.5%, and 61.9% on the same datasets. The inference speed of the model satisfied the real-time requirements. Compared to the YOLOv5 series models, the CSE-YOLOv5 model exhibited significant performance enhancements and surpassed other mainstream models in object detection.
The segmentation accuracy of substation equipment in infrared images captured by a UAV directly affects the results of thermal fault diagnosis. We proposed a multimodal path aggregation network (MPAN) that fuses visible and infrared images to address the problem of low segmentation accuracy of substation equipment in complex infrared backgrounds. First, we extracted and fused the features of two modal images, and considering the differences in the feature space of the two modal images, we proposed the adaptive feature fuse module (AFFM) to fuse the two modal features fully. We added a bottom-up pyramid network to the backbone with multi-scale features and a laterally connected path enhancement. Finally, we used dice coefficients to optimize the mask loss function. The experimental results showed that the fusion of multimodal images can enhance the segmentation performance and verify the effectiveness of the proposed modules, which can significantly improve the accuracy of the segmentation of substation equipment instances in infrared images.
We proposed a parameter self-tuning bi-histogram equalization method to solve saturation and detail loss in infrared image enhancement. We decomposed an input image into two independent sub-images according to the golden ratio of the gray cumulative probability density and modified each sub-image histogram through a multi-scale adaptive weighing process with input image exposure and sub-image gray-level interval information. Subsequently, we performed the equalization of the two corrected sub-histograms independently and combined the two equalized sub-images into a single output image. A test on 100 infrared images in a public dataset-INFRARED100 showed that, compared with brightness preserving bi-histogram equalization (BBHE), bi-histogram equalization with a plateau limit (BHEPL), and exposure-based sub-image histogram equalization (ESIHE), the images enhanced by the proposed method have appropriate contrast and greater average information entropy. We increased the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, and absolute mean brightness error (AMBE) by at least 17.2%, 4.0%, and 56.2% on average. The experiments illustrated that the proposed method is adaptable to infrared images with different brightness characteristics, effectively improving the contrast between the infrared image object and background. This method is superior to noise suppression, brightness, and detail preservation methods.
We proposed a two-scale image-fusion method for infrared and visible light image fusion based on guided filtering to reduce the complexity of multi-scale decomposition fusion algorithms and improve the adaptability of fused images to human visual characteristics. First, we used guided filtering to enhance the visible image and decomposed the source images into base and detail layers using guided filtering. In the fusion rules of the detail layer, we adopted the energy protection methods and detail extraction. Finally, we combined the fused detail layer with the base layer to synthesize the fusion results. The experimental results showed that the proposed method improves the visual effect, detail processing, and edge protection. We discussed the impact of visible image enhancement on fusion methods from experimental data. Enhancement can improve the fusion effect, but the fusion method is key in image fusion.
In this study, we proposed an infrared and visible image fusion algorithm that combines PIE and CGAN to make unmanned agricultural machinery perceive environmental information promptly and avoid accidents during production in complex environments. First, we trained the CGAN using an infrared image and corresponding saliency regions. The infrared image is input into the trained network to obtain the saliency region mask. After morphological optimization, we performed image fusion based on the PIE. Finally, we enhanced the fusion results by contrast processing. This algorithm can realize fast image fusion and satisfy the requirements for real-time environmental perception of unmanned agricultural machines. In addition, the algorithm retains the details of visible images and highlights important information concerning humans and animals in infrared images. It performs well in standard deviation and information entropy.
Telephoto common optical path imaging components are widely used in photoelectric reconnaissance pods, and the technical development of telephoto common optical path fast mirrors for composite axis image stabilization has become an inevitable trend. This study introduced the main components of telephoto common optical path imaging components. We realized the composite axis control and flyback compensation control strategy based on a fast mirror and analyzed and calculated its working timing and key parameters. We developed a fast mirror based on a telephoto common optical path imaging device, and simultaneously realized secondary image stabilization and flyback compensation within one frame of the image. We improved the reconnaissance range, image stabilization accuracy, and the search effect of medium- and high-altitude photoelectric reconnaissance pods.
We can use the infrared radiation characteristics of a target for target recognition. Data on infrared radiation characteristics obtained by out-field infrared imaging equipment is significant in evaluating early warning, reconnaissance, and stealth effects. It is difficult to obtain the response coefficients of out-field infrared imaging equipment. We introduced and compared radiometric calibration methods using a collimator and an extended-area blackbody. We conducted experiments using different calibration methods and then provided response coefficients of the out-field infrared imaging equipment. The long-distance radiometric calibration results showed different response coefficients at different distances. An infrared imaging system conducted calibration experiments with different working times and fusions. The radiometric out-of-focus calibration results showed that diffusion is not the main factor influencing calibration. Calibration experiments for different working times also showed that the response coefficients remained unchanged. The factors affecting the radiometric calibration of the out-field infrared imaging equipment are environmental radiation, path radiation, and path transmission. Short-distance radiometric calibration using an extended-area blackbody is necessary to obtain the response coefficients of the out-field infrared imaging equipment. If the radiometric calibration distance is less than 10 m, the error between the short- and long-distance radiometric calibrations is approximately 5%. This research helps out-field radiometric calibration of ground-based infrared imaging equipment and designs a radiometric calibration–measuring system.
In this study, the surface processing of cadmium zinc telluride (CZT) substrates was studied, which revealed surface dislocation defects. The surface processing mechanism and influence of the process parameters on the surface of the CZT substrates, including mechanical grinding, mechanical polishing, chemical mechanical polishing, and chemical polishing, are presented. Moreover, three types of chemical etchants, Everson, Nakagawa, and EAg, which reveal dislocation defects on the surface of CdZnTe with different crystal orientations, were also investigated.
Aiming at the problems of low recognition accuracy and slow detection speed of existing metal oxide arrester (MOA) infrared image fault detection methods, a MOA infrared image fault detection method based on improved YOLOv3 is proposed. Firstly, darknet19 network is used to replace the original darknet53 network of YOLOv3. During feature learning, the target frames in MOA images are analyzed by K-means clustering algorithm according to different MOA length width ratios in samples. The anchor frames in the center of samples are re clustered to get the appropriate number and size of anchor frames. Finally, the improved YOLOv3 model is used to complete the MOA infrared image fault detection. The experimental results show that the recognition accuracy of the improved model reaches 96.3%, and the recognition speed is 6.75ms.
在一些关键的军事和民用红外成像应用领域,待突破的技术瓶颈往往都集中在红外弱小目标检测技术上.简介了红外弱小目标检测的含义和在军事、民用方面的意义,重点综述了目前红外弱小目标检测的各类典型算法原理和特点,最后对红外弱小目标检测技术的研究和发展趋势进行了预测.
随着红外探测技术的迅速发展,如何提高军事目标的红外隐身能力成为一个亟待解决的难题,研究红外隐身材料有着十分重要的意义。本文简要分析了红外隐身材料的隐身机理,综述了低红外发射率材料、控温材料、光子晶体以及智能红外隐身材料等4类红外隐身材料近年来的研究现状,并展望了红外隐身材料未来的发展趋势。
非制冷红外焦平面探测器是热成像系统的核心部件。介绍了非制冷红外焦平面探测器的工作原理及微测辐射热计、读出电路、真空封装三大技术模块,分析了影响其性能的关键参数。与微测辐射热计设计相关的重要参数包括低的热导、高的红外吸收率、合适的热敏材料等;读出电路的传统功能是实现信号的转换读出,近年来也逐渐加入了信号补偿的功能;真空封装技术包括了金属管壳封装、陶瓷管壳封装、晶圆级封装和像元级封装。列举了国内外主要厂商的非制冷红外焦平面探测器的技术指标及近年来的最新技术进展,总结了非制冷红外焦平面探测器的技术发展趋势。
红外热波成像是近年来发展较快的一种新型无损检测技术,它是一门跨学科、跨应用领域的通用型实用技术,其三大核心技术包括热激励、红外图像采集及红外图像处理.本文对热激励技术中的闪光灯、激光、卤素灯、红外灯、超声、电磁等几种主要热激励方法的特点及研究现状进行了介绍与对比,分析了采集技术中的制冷与非制冷热像仪各自特点,并对红外图像处理技术中的降噪、增强、序列热图处理及缺陷提取等四大研究方向进行了总结,介绍了相应发展状况和进展.最后总结了该技术的发展趋势.
为了减少红外测温仪的测量误差,提高红外测温仪的测温精度,分析了距离、发射率和外界环境温度等因素对红外测温仪测温的影响;建立了红外测温实验系统采集测温数据,并对采集到的实验数据进行了分析验证,通过分析验证可得距离因素对红外辐射测温精度有较大的影响,并且存在一定的关系,从而为提高红外测温精度的提供了依据;设计了一套提高红外测温仪测量精度的系统,该系统能够测出被测物与红外测温仪之间的距离,根据测出的结果得到距离补偿公式,然后依据公式得出温度的距离补偿,从而得到物体的实际温度.最后分析可得,红外测温仪的测量精度能够大幅提高.
基于飞行时间法(Time-of-Flight,TOF)的3D相机是一种体积小、误差小、抗干扰能力强、可直接输出深度信息的新型立体成像设备。目前,该类相机已成为测量成像领域的研究热点。本文首先介绍了TOF相机的发展历程及测量原理;随后对TOF相机测量误差来源及类型进行分析;接着将TOF技术与其他主流的三维成像技术进行对比分析;最后对TOF相机的应用与发展趋势进行了阐述。
为了解决高动态红外图像在常规显示设备上显示时容易出现图像整体对比度低、弱小目标细节模糊等问题,提出了一种基于引导滤波图像分层的红外图像细节增强算法,并从算法理论分析和仿真结果两方面验证了引导滤波具有更好的边缘保持能力,能有效避免增强后出现"伪边缘"的缺陷.另外,针对原始全局的引导滤波算法对整幅图像各个区域使用相同的规整化因子,容易产生"光晕"现象的缺陷,本文在局部方差加权引导滤波算法的思想上,提出了基于LoG边缘算子的加权引导滤波算法.实验结果表明本文算法具有良好的细节增强效果,特别是对图像中的弱小目标;另外,相比目前应用广泛的双边滤波算法,本文算法运行时间要快得多,具有实时处理的应用前景.
中科院上海技物所近十年来开展了高性能短波红外 InGaAs 焦平面探测器的研究。0.9~1.7?m近红外 InGaAs 焦平面探测器已实现了256×1、512×1、1024×1等多种线列规格,以及320×256、640×512、4000×128等面阵,室温暗电流密度<5 nA/cm2,室温峰值探测率优于5×1012 cm?Hz1/2/W。同时,开展了向可见波段拓展的320×256焦平面探测器研究,光谱范围0.5~1.7?m,在0.8?m 的量子效率约20%,在1.0?m 的量子效率约45%。针对高光谱应用需求,上海技物所开展了1.0~2.5?m 短波红外 InGaAs 探测器研究,暗电流密度小于10 nA/cm2@200 K,形成了512×256、1024×128等多规格探测器,峰值量子效率高于75%,峰值探测率优于5×1011 cm?Hz1/2/W。
对红外探测器不断增长和提高的需求催生了第三代红外焦平面探测器技术。根据第三代红外探测器的概念,像素达到百万级,热灵敏度NETD达到1 mK量级是第三代制冷型高性能红外焦平面探测器的基本特征。计算结果表明读出电路需要达到1000 Me-以上的电荷处理能力和100 dB左右的动态范围(Dynamic Range)才能满足上述第三代红外焦平面探测器需求。提出在像素内进行数字积分技术,以期突破传统模拟读出电路的电荷存储量和动态范围瓶颈限制,使高空间分辨率、高温度分辨率及高帧频的第三代高性能制冷型红外焦平面探测器得到实现。
提出了一种基于梯度信息的结构相似性算法改进的红外图像非局部均值滤波方法.传统的非局部均值滤波算法采用欧氏距离度量图像块之间的相似性,因而不能够很好地衡量图像细节和边缘信息,导致滤波后图像模糊失真.针对此问题,采用结构相似性度量(structural similarity,SSIM)算法对欧氏距离进行加权改进,针对普通的SSIM边缘信息评价能力的不足,提出了带有梯度信息的GSSIM算法,实验结果表明本方法在保持非局部均值(Non-Local Means,NLM)滤波算法去噪能力的同时还能够较好地保持图像的边缘和细节信息.
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Monthly, Established 1979
Competent Authorities:China North Industries Group Corporation
Sponsored by:Kunming Institute of Physics
China Ordnance Society, Speciality
ISSN:1001-8891
CN:53-1054/TN
Postal distribution code:64-26
Editorial Office:No.31 Jiao Chang Dong Road, Kunming, 650223, China
Tel:0871-65105248
E-mail:irtek@china.com
Infrared technology is one of the earliest photoelectronic journals in China.Infrared Technology is published by Science Press, and it is a single monthly technical journal.
Infrared Technology is a professional and academic journal based on scientific research, which comprehensively reflects the research progress of infrared technology at home and abroad and its application in national defense, industry, agriculture and national economy.After years of efforts, INFRARED Technology has become the core journal of Chinese, The core journal of Chinese science and technology, and the source journal of Chinese Science citation database.