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Journal articles on the topic 'Remote detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 March 2023

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1

Wei Huang, Wei Huang, Jianyong Ma Jianyong Ma, Feng Zhu Feng Zhu, Jin Wang Jin Wang, and Changhe Zhou Changhe Zhou. "Low divergent diffractive optical element for remote detection." Chinese Optics Letters 12, no. 7 (2014): 070501–70503. http://dx.doi.org/10.3788/col201412.070501.

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Xie, Jing, Erik Stensrud, and Torbjørn Skramstad. "Detection-Based Object Tracking Applied to Remote Ship Inspection." Sensors 21, no. 3 (2021): 761. http://dx.doi.org/10.3390/s21030761.

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We propose a detection-based tracking system for automatically processing maritime ship inspection videos and predicting suspicious areas where cracks may exist. This system consists of two stages. Stage one uses a state-of-the-art object detection model, i.e., RetinaNet, which is customized with certain modifications and the optimal anchor setting for detecting cracks in the ship inspection images/videos. Stage two is an enhanced tracking system including two key components. The first component is a state-of-the-art tracker, namely, Channel and Spatial Reliability Tracker (CSRT), with improve

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Shin, Su-Jin, Seyeob Kim, Youngjung Kim, and Sungho Kim. "Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images." Remote Sensing 12, no. 17 (2020): 2734. http://dx.doi.org/10.3390/rs12172734.

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Detecting objects such as aircraft and ships is a fundamental research area in remote sensing analytics. Owing to the prosperity and development of CNNs, many previous methodologies have been proposed for object detection within remote sensing images. Despite the advance, using the object detection datasets with a more complex structure, i.e., datasets with hierarchically multi-labeled objects, is limited to the existing detection models. Especially in remote sensing images, since objects are obtained from bird’s-eye view, the objects are captured with restricted visual features and not always

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Blackmon, Fletcher A., and Lynn T. Antonelli. "Remote voice detection system." Journal of the Acoustical Society of America 127, no. 2 (2010): 1175. http://dx.doi.org/10.1121/1.3326912.

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Xu, Danqing, and Yiquan Wu. "Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection." Sensors 20, no. 15 (2020): 4276. http://dx.doi.org/10.3390/s20154276.

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Remote sensing targets have different dimensions, and they have the characteristics of dense distribution and a complex background. This makes remote sensing target detection difficult. With the aim at detecting remote sensing targets at different scales, a new You Only Look Once (YOLO)-V3-based model was proposed. YOLO-V3 is a new version of YOLO. Aiming at the defect of poor performance of YOLO-V3 in detecting remote sensing targets, we adopted DenseNet (Densely Connected Network) to enhance feature extraction capability. Moreover, the detection scales were increased to four based on the ori

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Ma, Ying Zhao, Wei Li Jiao, and Wang Wei. "Cloud Detection in Landsat5 Images Based on Template Gradient." Advanced Materials Research 271-273 (July 2011): 205–10. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.205.

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Cloud is an important factor affect the quality of optical remote sensing image. How to automatically detect the cloud cover of an image, reduce of useless data transmission, make great significance of higher data rate usefulness. This paper represent a method based on Lansat5 data, which can automatically mark the location of clouds region in each image, and effective calculated for each cloud cover, remove useless remote sensing images.

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Mohanad, Abdulhamid, and Peter Deng. "REMOTE HEALTH MONITORING: FALL DETECTION." TECHNICAL SCIENCES AND TECHNOLOGIES, no. 1(19) (2020): 199–205. http://dx.doi.org/10.25140/2411-5363-2020-1(19)-199-205.

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Urgency of the research. Falling is a serious health issue among the elderly population; it can result in critical injuries like hip fractures. Immobilization caused by injury or unconsciousness means that the victim cannot summon help themselves. Target setting. The target of this paper is to design and create a fall detection system. The system consists of a monitoring device that links wirelessly with a laptop. The device is able to accurately distinguish between fall and non-fall. Actual scientific researches and issues analysis. Healthcare systems in the world have undergone tremendous ev

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Rock, B. N., J. E. Vogelmann, D. L. Williams, A. F. Vogelmann, and T. Hoshizaki. "Remote Detection of Forest Damage." BioScience 36, no. 7 (1986): 439–45. http://dx.doi.org/10.2307/1310339.

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Dominici, Donatella, and Sara Zollini. "Remote Sensing in Coastline Detection." Journal of Marine Science and Engineering 8, no. 7 (2020): 498. http://dx.doi.org/10.3390/jmse8070498.

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UNUMA, MUNETOSHI. "A Remote Behaviour Detection System." Journal of the Institute of Electrical Engineers of Japan 119, no. 4 (1999): 228–31. http://dx.doi.org/10.1541/ieejjournal.119.228.

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Huang Zhen, 黄贞, and 吴林富 Wu Linfu. "Laser Remote Voice Detection System." Laser & Optoelectronics Progress 49, no. 12 (2012): 121206. http://dx.doi.org/10.3788/lop49.121206.

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12

O'Kane, L., R. Watt, T. Ledgeway, and R. Goutcher. "Remote Interactions in Contour Detection." i-Perception 2, no. 3 (2011): 192. http://dx.doi.org/10.1068/i192.

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13

Fan, Dian, Jianmin Gong, Bo Dong, and Anbo Wang. "Remote CO2 leakage detection system." Optical Engineering 52, no. 1 (2013): 010502. http://dx.doi.org/10.1117/1.oe.52.1.010502.

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14

Hatfield, J. L. "Remote Detection of Plant Stress." Phytopathology 80, no. 1 (1990): 37. http://dx.doi.org/10.1094/phyto-80-37.

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15

Dietmann, Sabine, Narcis Fernandez-Fuentes, and Liisa Holm. "Automated detection of remote homology." Current Opinion in Structural Biology 12, no. 3 (2002): 362–67. http://dx.doi.org/10.1016/s0959-440x(02)00332-9.

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Gagnon, R. E., J. Groves, and W. Pearson. "Remote ice detection equipment — RIDE." Cold Regions Science and Technology 72 (March 2012): 7–16. http://dx.doi.org/10.1016/j.coldregions.2011.11.004.

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Wachter, E. A., T. Thundat, P. I. Oden, R. J. Warmack, P. G. Datskos, and S. L. Sharp. "Remote optical detection using microcantilevers." Review of Scientific Instruments 67, no. 10 (1996): 3434–39. http://dx.doi.org/10.1063/1.1147149.

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18

ABDULHAMID, Mohanad, and Deng PETER. "REMOTE HEALTH MONITORING: FALL DETECTION." Applied Computer Science 16, no. 1 (2020): 95–102. http://dx.doi.org/10.35784/acs-2020-08.

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Falling is a serious health issue among the elderly population; it can result in critical injuries like hip fractures. Immobilization caused by injury or unconsciousness means that the victim cannot summon help themselves. With elderly who live alone, not being found for hours after a fall is quite common and drastically increases the significance of fall-induced injuries. With an aging Baby Boomer population, the incidence of falls will only rise in the next few decades. The objective of this paper is to design and create a fall detection system. The system consists of a monitoring device tha

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19

DRĂGHICI, Andreea, and Titus BĂLAN. "REMOTE DETECTION AND TRACKING OF ALCOHOL CONCENTRATION FOR CAR DRIVERS." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 19, no. 1 (2017): 263–68. http://dx.doi.org/10.19062/2247-3173.2017.19.1.30.

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20

Chen, Haiyan. "High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology." Advances in Multimedia 2022 (August 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/6095308.

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We provide remote sensing image enhancement technology based on 6G nursing urine technology to solve the main problems and current challenges of target detection in remote sensing imaging. High-resolution images: to get a good image, first look at the remote control image, and objective findings on the remote control image play an important role in this process. Military and civilian use: at present, many advanced object detection algorithms have achieved success in natural imaging, but their development is limited by two factors: large variation in object size and low detection accuracy, the

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Rosle, Rhushalshafira, Nik Norasma Che’Ya, Yuhao Ang, et al. "Weed Detection in Rice Fields Using Remote Sensing Technique: A Review." Applied Sciences 11, no. 22 (2021): 10701. http://dx.doi.org/10.3390/app112210701.

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This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing p

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Listowski, Constantino, Edouard Forestier, Stavros Dafis, et al. "Remote Monitoring of Mediterranean Hurricanes Using Infrasound." Remote Sensing 14, no. 23 (2022): 6162. http://dx.doi.org/10.3390/rs14236162.

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Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year over the waters of the Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructure and lives because of their strong winds and intense rainfall. Infrasound technology has already been employed to investigate the acoustic signatures of severe weather events, and this study aims at characterizing, for the first time, the infrasound detections that can be related to medicanes. This work also contributes to infrasound source discrimination efforts in the context of the Compre

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23

Jiang, Wei, Xianda Wu, Xiang Cui, and Chaoge Liu. "A Highly Efficient Remote Access Trojan Detection Method." International Journal of Digital Crime and Forensics 11, no. 4 (2019): 1–13. http://dx.doi.org/10.4018/ijdcf.2019100101.

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Nowadays, machine learning is popular in remote access Trojan (RAT) detection which can create patterns for decision-making. However, most research focus on improving the detection rate and reducing the false negative rate, therefore they ignore the result of abnormal samples. In addition, most classifiers select several proprietary applications and RATs as their training set, which makes them difficult to adapt to the real environment. In this article, the authors address the issue of imbalance dataset between normal and RAT samples, and propose a highly efficient method of detecting RATs in

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Shimoi, Nobuhiro, and Yoshihiro Takita. "Mine Remote Sensing Using a Working Robot." Journal of Robotics and Mechatronics 17, no. 1 (2005): 101–5. http://dx.doi.org/10.20965/jrm.2005.p0101.

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In conducting mine detection experiments using our prototype robot COMET-1, we developed end effectors on the robot’s working legs. When detecting a mine, a robot must step safely and stably without hitting it. For this study, we created a simulation model to test the movement of a robot having an optical proximity sensor on each foot and used a walking algorithm having compliance control. We verified its efficiency in walking experiments. We also studied the use of remote sensing technology with an IR camera combined with other sensors. Tests with trial mines were used to study the detection

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25

Xu, Danqing, and Yiquan Wu. "MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection." Remote Sensing 12, no. 19 (2020): 3118. http://dx.doi.org/10.3390/rs12193118.

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High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreove

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26

Yang, Lei, Guowu Yuan, Hao Zhou, Hongyu Liu, Jian Chen, and Hao Wu. "RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images." Applied Sciences 12, no. 17 (2022): 8707. http://dx.doi.org/10.3390/app12178707.

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Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model is named RS-YOLOX. To strengthen the feature learning ability of the network, we used Efficient Channel Attention (ECA) in the backbone network of YOLOX and combined the Adaptively Spatial Feature Fusion (ASFF) with the neck network of YOLOX. To balance the numbers of positive an

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27

Guo, Fu Qin, and Yuan Qing Wang. "Study of Remote Sensing and Remote Sensing Robot Technology." Applied Mechanics and Materials 214 (November 2012): 914–18. http://dx.doi.org/10.4028/www.scientific.net/amm.214.914.

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Remote sensing robot technology is an emerging research area, which attains a development in recent years. In this paper, the definitions of remote sensing and robot remote sensing will be discussed, and then the remote mines detection technology of RAT-1 eight-wheeled robot, the application of IR-optical sensor in mobile robot for detection and source location of gas leakage, the monitoring of environment intelligent robot on environment and the positioning of mobile robot on targets and other focuses are emphatically introduced, and finally the important problems in each application as well

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28

Dinesh, S. "Reducing Dimensionality in Remote Homology Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 1052–54. http://dx.doi.org/10.22214/ijraset.2021.39417.

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Abstract: Homology detection plays a major role in bioinformatics. Different type of methods is used for Homology detection. Here we extract the information from protein sequences and then uses the various algorithm to predict the similarity between protein families. SVM most commonly used the algorithm in homology detection. Classification techniques are not suitable for homology detection because theyare not suitable for high dimensional datasets. Soreducing the higher dimensionality is very important than easily can predict the similarity of protein families. Keywords: Homology detection, P

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29

Wang, Jielei, Zongyong Cui, Zhipeng Zang, Xiangjie Meng, and Zongjie Cao. "Absorption Pruning of Deep Neural Network for Object Detection in Remote Sensing Imagery." Remote Sensing 14, no. 24 (2022): 6245. http://dx.doi.org/10.3390/rs14246245.

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In recent years, deep convolutional neural networks (DCNNs) have been widely used for object detection tasks in remote sensing images. However, the over-parametrization problem of DCNNs hinders their application in resource-constrained remote sensing devices. In order to solve this problem, we propose a network pruning method (named absorption pruning) to compress the remote sensing object detection network. Unlike the classical iterative three-stage pruning pipeline used in existing methods, absorption pruning is designed as a four-stage pruning pipeline that only needs to be executed once, w

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Liu, Yun, and Jia-Bao Liu. "Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network." Computational Intelligence and Neuroscience 2022 (May 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/9223552.

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Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were u

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Dunaeva, A. V., and F. A. Kornilov. "Building detection in remote sensing images using a digital surface model." Computational Mathematics and Information Technologies 2 (2017): 185–93. http://dx.doi.org/10.23947/2587-8999-2017-2-185-193.

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Suresh, Shaina, Mia Torres-Dela Cruz, and Deepak T. J. "Remote Sensing and Unmanned Aerial Device for Early Forest Fire Detection." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-ICAEIT2017 (2018): 58–62. http://dx.doi.org/10.31142/ijtsrd19131.

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Yang, Le, Yiming Chen, Shiji Song, Fan Li, and Gao Huang. "Deep Siamese Networks Based Change Detection with Remote Sensing Images." Remote Sensing 13, no. 17 (2021): 3394. http://dx.doi.org/10.3390/rs13173394.

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Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection datas

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34

Gashnikov, M. V., and A. V. Kuznetsov. "Detection of artificial fragments embedded in remote sensing images by adversarial neural networks." Computer Optics 46, no. 4 (2022): 643–49. http://dx.doi.org/10.18287/2412-6179-co-1064.

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We investigate algorithms for detecting artificial fragments of remote sensing images generated by adversarial neural networks. We consider a detector of artificial images based on the detection of a spectral artifact of generative-adversarial neural networks that is caused by a layer for enhancing the resolution. We use the detecting algorithm to detect artificial fragments embedded in natural remote sensing images using an adversarial neural network that includes a contour generator. We use remote sensing images of various types and resolutions, whereas the substituted areas, some being not

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Mao, Jiali, Jiaye Liu, Cheqing Jin, and Aoying Zhou. "Feature Grouping–based Trajectory Outlier Detection over Distributed Streams." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (2021): 1–23. http://dx.doi.org/10.1145/3444753.

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Owing to a wide variety of deployment of GPS -enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we focus on the issue of outlier detection over distributed trajectory streams, where the outliers refer to a few entities whose motion behaviors are significantly different from their local neighbors. In view of skewed distribution property and evolving nature of trajectory data, and on-the-fly detection requirement over distributed streams, we f

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Belov, M. L., Ya E. Drachennikova, and V. A. Gorodnichev. "Laser Remote Sensing Method of Carbon Monoxide Emissions Detection." Radio Engineering, no. 3 (June 21, 2020): 20–34. http://dx.doi.org/10.36027/rdeng.0320.0000170.

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Monitoring of atmospheric gas pollution is one of the most important environmental target. Laser methods are the most effective for remote operational monitoring of atmospheric pollution.One of the most important air pollutants is carbon monoxide.The article analyzes the possibility of laser remote sensing method of carbon monoxide emissions detection in atmosphere.The information parameter measured by the remote sensing laser gas analyzer was assessed for absorption band of carbon monoxide near 2,3 μm.The information parameter that can be used for monitoring monoxide emissions is the ratio of

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Salem, Mohammed A. M., and Sultan Almotairi. "Vehicle Detection In Remote Sensing Images." International Journal of Innovative Technology and Exploring Engineering 8, no. 11 (2019): 928–33. http://dx.doi.org/10.35940/ijitee.k1807.0881119.

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Traffic monitoring and management is one of the most crucial tasks of governing bodies in modern big cities. With each passing day the traffic problem grows in complexity due to the continuous increase of participating vehicles and the hard expansion of the road network and parking places. In this article we introduce a new method for vehicle detection and localization in parking lots using high resolution UAV images. In order to end up with practical and yet effective approach, which could be implemented on low computing hardware resources and integrated with the camera in the UAV, we conside

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Gashnikov, M. V., and A. V. Kuznetsov. "Detection of Fake Remote-Sensing Data." Optical Memory and Neural Networks 31, no. 1 (2022): 16–21. http://dx.doi.org/10.3103/s1060992x22010052.

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39

KHALIFA, IRAKY, and ABDUALRHMAN ALZIDAN. "VEHICLE DETECTION FROM REMOTE SENSING IMAGES." Nuclear Sciences Scientific Journal 2, no. 1 (2013): 133–37. http://dx.doi.org/10.21608/nssj.2013.30989.

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Lyalko, V. I., O. I. Sakhatsky, G. M. Zholobak, and A. A. Apostolov. "Remote detection of sunflower sowing time." Kosmìčna nauka ì tehnologìâ 19, no. 2(81) (2013): 74–78. http://dx.doi.org/10.15407/knit2013.02.074.

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Fenn, R., and J. C. Watson. "Remote edge detection of subcutaneous stiffeners." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 219, no. 8 (2005): 579–85. http://dx.doi.org/10.1243/095440505x32490.

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Construction of narrow-gap double skin components requires, at some stage, blind welding from one side. During construction, due to thermal distortion, the hidden stiffeners (spacers or stringers) may move sufficiently far from their designated locations that assembly welds, made from one side, could miss the stringers completely. Thus, a real-time sensor capable of identifying and accurately locating spacer edges beneath the outer skin is required. Outer skin magnetic properties and plate/spacer separation seriously influence the capabilities of the best candidate detecting methods. Initial t

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Wang, Wenjin, Sander Stuijk, and Gerard de Haan. "Unsupervised Subject Detection via Remote PPG." IEEE Transactions on Biomedical Engineering 62, no. 11 (2015): 2629–37. http://dx.doi.org/10.1109/tbme.2015.2438321.

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Oniani, Salome, and Ia Mosashvili. "Remote detection of automated system verification." Works of Georgian Technical University, no. 1(515) (March 26, 2020): 106–14. http://dx.doi.org/10.36073/1512-0996-2020-1-106-114.

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van Gastel, Mark, Sander Stuijk, and Gerard de Haan. "Robust respiration detection from remote photoplethysmography." Biomedical Optics Express 7, no. 12 (2016): 4941. http://dx.doi.org/10.1364/boe.7.004941.

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Mishra, Shivangi, Priyanka Shrivastava, and Priyanka Dhurvey. "Change Detection Techniques in Remote Sensing." JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 6, no. 2 (2016): 51. http://dx.doi.org/10.14801/jaitc.2016.6.2.51.

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Voinier, C., C. H. Skinner, and A. L. Roquemore. "Electrostatic dust detection on remote surfaces." Journal of Nuclear Materials 346, no. 2-3 (2005): 266–71. http://dx.doi.org/10.1016/j.jnucmat.2005.06.018.

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Mishchenko, Michael I., Gorden Videen, Vera K. Rosenbush, and Yaroslav S. Yatskiv. "Polarimetric detection, characterization, and remote sensing." Journal of Quantitative Spectroscopy and Radiative Transfer 112, no. 13 (2011): 2042–45. http://dx.doi.org/10.1016/j.jqsrt.2011.04.004.

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Maathuis, B. H. P. "Remote Sensing Based Detection of Minefields." Geocarto International 18, no. 1 (2003): 51–60. http://dx.doi.org/10.1080/10106040308542263.

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FUJII, SHUJI, KWANG YOUNG KIM, TATSUYA SUNAGAWA, and ICHIYA HAYAKAWA. "LASER REMOTE DETECTION FOR PARTICLE CONCENTRATION." Journal of Architecture, Planning and Environmental Engineering (Transactions of AIJ) 379 (1987): 9–16. http://dx.doi.org/10.3130/aijax.379.0_9.

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Alesheikh, A. A., A. Ghorbanali, and N. Nouri. "Coastline change detection using remote sensing." International Journal of Environmental Science & Technology 4, no. 1 (2007): 61–66. http://dx.doi.org/10.1007/bf03325962.

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