Journal of Infrared, Millimeter, and Terahertz Waves最新文献_第10页

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-05-09 DOI: 10.1117/12.2675443

引用次数: 0

A 77-GHz Down-Conversion Mixer with +18.4 dB High Gain, +12.2 dBm OIP3, and Low Noise in 90-nm CMOS Technology 77 ghz下变频混频器，+18.4 dB高增益，+12.2 dBm OIP3，低噪声，采用90纳米CMOS技术

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-13 DOI: 10.1007/s10762-023-00917-2

Hua-Bin Zhang, Sida Tang, Mengye Cai, Yanfeng Jiang

引用次数: 0

Shadow tracking for airborne terahertz video-SAR based on SORT algorithm 基于SORT算法的机载太赫兹视频sar阴影跟踪

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-12 DOI: 10.1117/12.2662363

H. Wu, Fuwei Wu, S. Shang, Zhenhua Liu, Yuhao Yang, Dasheng Li, Pin Li

引用次数: 0

Research on target intrusion detection algorithm based on improved ViBe using infrared thermal imaging 基于改进ViBe的红外热成像目标入侵检测算法研究

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-12 DOI: 10.1117/12.2662528

Lin Sun, Qun Ma, Yue Zhao, Bing Liu, Tianhua Zhang, Heng Yang

引用次数: 0

A cross-age face generation method based on CGAN and LSTM 基于CGAN和LSTM的跨年龄人脸生成方法

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-12 DOI: 10.1117/12.2662598

Yunfei Cheng, Yuexia Liu, Wen Wang

引用次数: 0

A novel method of heterologous image registration based on SURF and feature inertial following 一种基于SURF和特征惯性跟随的异源图像配准新方法

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-12 DOI: 10.1117/12.2661631

S. Shang, Zhan-He Ou, Yang Zhou, Yuhao Yang, Pin Li

引用次数: 0

Infrared small target detection based on local image alignment in complex background 复杂背景下基于局部图像对准的红外小目标检测

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-12 DOI: 10.1117/12.2663167

Fan Wang, Weixian Qian

{"title":"Infrared small target detection based on local image alignment in complex background","authors":"Fan Wang, Weixian Qian","doi":"10.1117/12.2663167","DOIUrl":"https://doi.org/10.1117/12.2663167","url":null,"abstract":"Infrared (IR) small target detection is widely used in both civilian and military security fields. However, IR small target detection in complex backgrounds faces many challenges. For example, small targets often occupy only a few pixels in IR images, lacking texture and contour features. IR images are seriously disturbed by clutter and noise, which results in the target being easily submerged. Therefore, it is difficult to achieve a low false alarm rate and a high detection rate at the same time. In this paper, a small target detection method based on local image alignment is proposed. First, the thermal IR imaging system is combined with the range-gated technology. The range-gated technology can be used to shield the background of the non-gated area. Second, the continuous frames in the sequence are accumulated to suppress noise. The movement of the target will form a trailing line in the accumulated image, and the trailing line contains the motion information of the target. Third, the trailing line is detected and extracted. Each trailing line corresponds to a suspected target area, including the real target and edges in the background, and the motion information of the suspected target can be calculated from the length and direction of the trailing line. Then, according to the motion information, the local images of the suspected target in a certain number of consecutive frames are aligned and accumulated. In the accumulated image, the real small target will shrink to a bright spot, and the signal-to-noise ratio will be significantly improved, while the background edge still appears as a line. Finally, the target is extracted from the accumulated image.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"3 1","pages":"125653Q - 125653Q-4"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73597055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Faster-RCNN algorithm combined with infrared satellite image for tropical cyclone detection 结合红外卫星图像的改进Faster-RCNN算法用于热带气旋探测

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-12 DOI: 10.1117/12.2661625

Liu Zhang, Changjiang Zhang, Feng Guo, Wanle Zhao

{"title":"Improved Faster-RCNN algorithm combined with infrared satellite image for tropical cyclone detection","authors":"Liu Zhang, Changjiang Zhang, Feng Guo, Wanle Zhao","doi":"10.1117/12.2661625","DOIUrl":"https://doi.org/10.1117/12.2661625","url":null,"abstract":"Automatic detection of tropical cyclone (TC) regions from satellite images can provide regions of interest for intelligent TC positioning and intensity determination, and improve the efficiency and accuracy of intelligent disaster weather forecasting. There are currently few studies on automatic detection of TCs from satellite images. In recent years, deep learning technology has developed rapidly in various fields. This paper improves the Faster-RCNN target detection model in deep learning and applies it to the TC detection. The TC detection model designed in this paper is based on the original Faster-RCNN network framework, and the feature extraction network is changed from the original VGG16 network to the ResNet50 network . On this basis, this paper designs a feature fusion network Single Output Feature Fusion Networks (SOFFN). The feature layer used for detection can combine the semantic information of the high-level feature map and the high-resolution feature information of the low-level feature map, fuse different feature layers. At the same time, a new attention mechanism, Channel Linear Weighted Networks (CLWNet), based on the Squeeze-and-Excitation Networks (SENet) channel attention mechanism improvement is added to the model designed in this paper to improve the detection performance. In this paper, China's FY-2D satellite images are used to verify the performance of the proposed model. Experimental results show that the proposed model has achieved good results in TC detection.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"111 1","pages":"125650I - 125650I-6"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77611801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pixel-level temperature sensor design for image sensors 像素级温度传感器的图像传感器设计

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-12 DOI: 10.1117/12.2664572

Chiyuan Zhang, Nan Chen, L. Yao, Shengyou Zhong, Jiqing Zhang, Changkun Cui

{"title":"Pixel-level temperature sensor design for image sensors","authors":"Chiyuan Zhang, Nan Chen, L. Yao, Shengyou Zhong, Jiqing Zhang, Changkun Cui","doi":"10.1117/12.2664572","DOIUrl":"https://doi.org/10.1117/12.2664572","url":null,"abstract":"With the development of science and technology, image sensors are more and more widely used, such as digital cameras and surveillance cameras. However, due to the physical characteristics of the photodetector, which performance is sensitive to the variation of the operating temperature. Therefore, a digital temperature sensor integrated on the chip is required to measure the operating temperature and assist in correction and compensation. Traditional scheme integrates one temperature sensor on the whole image sensor chip, which can’t reflect the temperature distribution for each pixel. It’s desirable to implement temperature measurement in pixel level for accurate correction, but existing temperature sensor occupying area of hundred μm2, which can’t be input to the pixel of image sensor. In additional, the power consumption of each temperature sensor is μW-level, which will dissipate considerable power for million temperature sensors. In this paper, a pixel-level integrated temperature sensor is proposed. The circuit is composed of only a capacitor and a conventional diode. The readout circuit is similar to that of the active pixel of image sensor, thus the ADC (Analog-to-Digital Converter) and other readout circuits and be multiplexed. The temperature sensor integrated in pixel is designed, which area is only 0.21 μm2. The simulation results show the increased power consumption for 50Hz working pixels don’t exceed 4%. It’s confirmed that the proposed pixel-level integrated temperature sensor can measure the temperature of each pixel and assisting in the accurate correction of image sensor in pixel level.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"26 1","pages":"125653X - 125653X-9"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81455120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fire detection algorithm of infrared thermal imaging in petrochemical area based on improved YOLOv4-tiny framework and time-domain feature analysis 基于改进YOLOv4-tiny框架和时域特征分析的石化区红外热像火灾探测算法

IF 2.9 3区工程技术

Journal of Infrared, Millimeter, and Terahertz Waves Pub Date : 2023-04-12 DOI: 10.1117/12.2661626

Qun Ma, Mei-rong Zhao, Lin Sun, Yue Zhao, Yelong Zheng, B. Liu

{"title":"Fire detection algorithm of infrared thermal imaging in petrochemical area based on improved YOLOv4-tiny framework and time-domain feature analysis","authors":"Qun Ma, Mei-rong Zhao, Lin Sun, Yue Zhao, Yelong Zheng, B. Liu","doi":"10.1117/12.2661626","DOIUrl":"https://doi.org/10.1117/12.2661626","url":null,"abstract":"Oil is one of the most important energy supplies for economic development. In recent years, the fire safety problems of petrochemical enterprises have become prominent, with serious casualties and property losses. The continuously monitoring of key areas through the low-cost and intelligent infrared thermal imaging video monitoring system has important engineering application significance for the improvement of petrochemical site safety problems. According to the characteristics of infrared thermal imaging fire target, this paper proposes a method of deep neural network combined with time-domain feature analysis to realize fire detection. Firstly, high thermal pixels are extracted from the infrared image, and the gray-scale image is converted into a binary gray-scale image. Based on the YOLOv4 tiny framework, multi-level channel prediction and attention mechanism are added to detect the fire candidate target of the binary image, Finally, the candidate target is finally determined by analyzing the time-domain characteristics. Compared with the traditional temperature threshold judgment infrared temperature measurement fire alarm system, it can achieve high detection rate and effectively reduce the false alarm rate of the system. The intelligent security monitoring system in Petrochemical area designed in this paper has been applied in practical engineering, and the fire detection effect is good, which realizes the requirements of low power consumption, low cost and high reliability of the security monitoring system in Petrochemical area based on infrared thermal imaging.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"31 1","pages":"125650J - 125650J-19"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81911967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0