乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T15:17:16Z","timestamp":1724167036927},"reference-count":45,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T00:00:00Z","timestamp":1635984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003759","name":"Technical University of Madrid","doi-asserted-by":"publisher","award":["PID2019-105808RB-I00"],"id":[{"id":"10.13039\/501100003759","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to the application and development of new and improved neural networks to vision models together with modern optical sensors that incorporate thermal cameras, capable of capturing data in post-disaster environments (PDE) with rustic conditions (low luminosity, suspended particles, obstructive materials). Due to the high risk posed by PDE because of the potential collapse of structures, electrical hazards, gas leakage, etc., primary intervention tasks such as victim identification are carried out by robotic teams, provided with specific sensors such as thermal, RGB cameras, and laser. The application of Convolutional Neural Networks (CNN) to computer vision is a breakthrough for detection algorithms. Conventional methods for victim identification in these environments use RGB image processing or trained dogs, but detection with RGB images is inefficient in the absence of light or presence of debris; on the other hand, developments with thermal images are limited to the field of surveillance. This paper\u2019s main contribution focuses on implementing a novel automatic method based on thermal image processing and CNN for victim identification in PDE, using a Robotic System that uses a quadruped robot for data capture and transmission to the central station. The robot\u2019s automatic data processing and control have been carried out through Robot Operating System (ROS). Several tests have been carried out in different environments to validate the proposed method, recreating PDE with varying conditions of light, from which the datasets have been generated for the training of three neural network models (Fast R-CNN, SSD, and YOLO). The method\u2019s efficiency has been tested against another method based on CNN and RGB images for the same task showing greater effectiveness in PDE main results show that the proposed method has an efficiency greater than 90%.<\/jats:p>","DOI":"10.3390\/s21217346","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T02:25:54Z","timestamp":1636079154000},"page":"7346","source":"Crossref","is-referenced-by-count":32,"title":["Autonomous Thermal Vision Robotic System for Victims Recognition in Search and Rescue Missions"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-2824-6611","authenticated-orcid":false,"given":"Christyan","family":"Cruz Ulloa","sequence":"first","affiliation":[{"name":"Centre for Automation and Robotics (CAR) Universidad Polit\u00e9cnica de Madrid\u2014Consejo Superior de Investigaciones Cient\u00edficas, 28006 Madrid, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3605-330X","authenticated-orcid":false,"given":"Guillermo","family":"Prieto S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Centre for Automation and Robotics (CAR) Universidad Polit\u00e9cnica de Madrid\u2014Consejo Superior de Investigaciones Cient\u00edficas, 28006 Madrid, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1691-3907","authenticated-orcid":false,"given":"Antonio","family":"Barrientos","sequence":"additional","affiliation":[{"name":"Centre for Automation and Robotics (CAR) Universidad Polit\u00e9cnica de Madrid\u2014Consejo Superior de Investigaciones Cient\u00edficas, 28006 Madrid, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4893-2571","authenticated-orcid":false,"given":"Jaime","family":"Del Cerro","sequence":"additional","affiliation":[{"name":"Centre for Automation and Robotics (CAR) Universidad Polit\u00e9cnica de Madrid\u2014Consejo Superior de Investigaciones Cient\u00edficas, 28006 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"ref_1","unstructured":"(2021, June 23). UNDRR Home. Available online: https:\/\/www.undrr.org\/."},{"key":"ref_2","unstructured":"(2021, June 23). Noticias ONU: Pese al Aumento de las Amenazas de Origen Natural en el Siglo XXI, los Pa\u00edses Siguen \u201cSembrando las Semillas de su Destrucci\u00f3n\u201d. Available online: https:\/\/news.un.org\/es\/story\/2020\/10\/1482242."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s43154-021-00048-3","article-title":"Multi-Agent Systems for Search and Rescue Applications","volume":"2","author":"Drew","year":"2021","journal-title":"Curr. Robot. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1002\/rob.21887","article-title":"The current state and future outlook of rescue robotics","volume":"36","author":"Delmerico","year":"2019","journal-title":"J. Field Robot."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Queralta, J.P., Taipalmaa, J., Pullinen, B.C., Sarker, V.K., Gia, T.N., Tenhunen, H., Gabbouj, M., Raitoharju, J., and Westerlund, T. (2020). Collaborative multi-robot systems for search and rescue: Coordination and perception. arXiv.","DOI":"10.1109\/ACCESS.2020.3030190"},{"key":"ref_6","first-page":"67","article-title":"Robots\u2026 assemble! Although humans and highly trained dogs will always be critical in search-and-rescue operations, robots are being developed\u2014Taking inspiration from the natural world\u2014That are helping rescue teams save lives","volume":"15","author":"Pozniak","year":"2020","journal-title":"Eng. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"582","DOI":"10.7210\/jrsj.22.582","article-title":"Survey on urban search and rescue robots","volume":"22","author":"Shah","year":"2004","journal-title":"J. Robot. Soc. Jpn."},{"key":"ref_8","first-page":"81","article-title":"Urban search and rescue robots: From tragedy to technology","volume":"17","author":"Davids","year":"2002","journal-title":"IEEE Intell. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"129","DOI":"10.4995\/riai.2019.11078","article-title":"A Comparative Study of Techniques for Hyperspectral Image Classification","volume":"16","author":"Paoletti","year":"2019","journal-title":"Rev. Iberoam. Autom. Inform. Ind."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chadwick, R.A. (2005, January 26\u201330). The impacts of multiple robots and display views: An urban search and rescue simulation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Orlando, FL, USA.","DOI":"10.1177\/154193120504900337"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"John, V., Mita, S., Liu, Z., and Qi, B. (2015, January 18\u201322). Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks. Proceedings of the 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, Japan.","DOI":"10.1109\/MVA.2015.7153177"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alsafasfeh, M., Abdel-Qader, I., Bazuin, B., Alsafasfeh, Q., and Su, W. (2018). Unsupervised fault detection and analysis for large photovoltaic systems using drones and machine vision. Energies, 11.","DOI":"10.3390\/en11092252"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tammana, A., Amogh, M., Gagan, B., Anuradha, M., and Vanamala, H. (2021). Thermal Image Processing and Analysis for Surveillance UAVs. Information and Communication Technology for Competitive Strategies (ICTCS 2020), Springer.","DOI":"10.1007\/978-981-16-0882-7_50"},{"key":"ref_14","first-page":"1","article-title":"Approach of Thermal Imaging as a Facial Recognition","volume":"2","author":"Tamboli","year":"2016","journal-title":"JournalNX"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhang, J., and Shen, C. (2010, January 26\u201329). Improved human detection and classification in thermal images. Proceedings of the 2010 IEEE International Conference on Image Processing, Hong Kong, China.","DOI":"10.1109\/ICIP.2010.5649946"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ilikci, B., Chen, L., Cho, H., and Liu, Q. (2019, January 26\u201328). Heat-Map Based Emotion and Face Recognition from Thermal Images. Proceedings of the 2019 Computing, Communications and IoT Applications (ComComAp), Shenzhen, China.","DOI":"10.1109\/ComComAp46287.2019.9018786"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3390\/s20010034","article-title":"CNN-based person detection using infrared images for night-time intrusion warning systems","volume":"20","author":"Park","year":"2020","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Portmann, J., Lynen, S., Chli, M., and Siegwart, R. (June, January 31). People detection and tracking from aerial thermal views. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907094"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cerutti, G., Prasad, R., and Farella, E. (2019, January 12\u201317). Convolutional neural network on embedded platform for people presence detection in low resolution thermal images. Proceedings of the ICASSP 2019-IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682998"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gomez, A., Conti, F., and Benini, L. (2018, January 8\u201310). Thermal image-based CNN\u2019s for ultra-low power people recognition. Proceedings of the 15th ACM International Conference on Computing Frontiers, Ischia, Italy.","DOI":"10.1145\/3203217.3204465"},{"key":"ref_21","unstructured":"Cerutti, G., Milosevic, B., and Farella, E. (2018, January 26\u201329). Outdoor People Detection in Low Resolution Thermal Images. Proceedings of the 2018 3rd International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-Bravo, D.M., Mutombo, P.M., Braem, B., and Marquez-Barja, J.M. (2020, January 14\u201316). Applying Faster R-CNN in Extremely Low-Resolution Thermal Images for People Detection. Proceedings of the 2020 IEEE\/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), Prague, Czech Republic.","DOI":"10.1109\/DS-RT50469.2020.9213609"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s43154-020-00011-8","article-title":"Using deep learning to find victims in unknown cluttered urban search and rescue environments","volume":"1","author":"Fung","year":"2020","journal-title":"Curr. Robot. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lygouras, E., Santavas, N., Taitzoglou, A., Tarchanidis, K., Mitropoulos, A., and Gasteratos, A. (2019). Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations. Sensors, 19.","DOI":"10.3390\/s19163542"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114195","DOI":"10.1016\/j.eswa.2020.114195","article-title":"A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data","volume":"167","author":"Cebollada","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ba\u00f1uls, A., Mandow, A., V\u00e1zquez-Mart\u00edn, R., Morales, J., and Garc\u00eda-Cerezo, A. (2020, January 4\u20136). Object Detection from Thermal Infrared and Visible Light Cameras in Search and Rescue Scenes. Proceedings of the 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/SSRR50563.2020.9292593"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hoshino, W., Seo, J., and Yamazaki, Y. (2021, January 12\u201316). A study for detecting disaster victims using multi-copter drone with a thermographic camera and image object recognition by SSD. Proceedings of the 2021 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Delft, The Netherlands.","DOI":"10.1109\/AIM46487.2021.9517524"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106697","DOI":"10.1016\/j.compeleceng.2020.106697","article-title":"Locating victims in hot environments using combined thermal and optical imaging","volume":"85","author":"Dawdi","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"254","DOI":"10.4995\/riai.2020.11806","article-title":"Unmanned aerial vehicle for fire surveillance and monitoring","volume":"17","author":"Madridano","year":"2020","journal-title":"Rev. Iberoam. Autom. Inform. Ind."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105348","DOI":"10.1016\/j.compag.2020.105348","article-title":"Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation","volume":"173","author":"Vasconez","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","unstructured":"Barrientos, A. (2021, June 29). TASAR\u2014Team of Advanced Search And Rescue Robots. Available online: https:\/\/www.car.upm-csic.es\/?portfolio=tasar."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kumar, A., Fu, Z., Pathak, D., and Malik, J. (2021). Rma: Rapid motor adaptation for legged robots. arXiv.","DOI":"10.15607\/RSS.2021.XVII.011"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1207\/s15327051hci1901&2_6","article-title":"Beyond usability evaluation: Analysis of human-robot interaction at a major robotics competition","volume":"19","author":"Yanco","year":"2004","journal-title":"Hum. Interact."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chmielewski, L.J., Kozera, R., Shin, B.S., and Wojciechowski, K. (2014). Noise Reduction in Thermal Images. Computer Vision and Graphics, Springer International Publishing.","DOI":"10.1007\/978-3-319-11331-9"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/0034-4257(92)90092-X","article-title":"Emissivity of terrestrial materials in the 8\u201314 \u03bcm atmospheric window","volume":"42","author":"Salisbury","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/0306-2619(82)90039-3","article-title":"Thermal emissivity of materials","volume":"12","author":"Raman","year":"1982","journal-title":"Appl. Energy"},{"key":"ref_37","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_39","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_40","unstructured":"Quiroga, H. (2017). Anaconda, Createspace Independent Publishing Platform."},{"key":"ref_41","unstructured":"Blokdyk, G. (2018). Tensorflow: A Complete Guide, 5starcooks."},{"key":"ref_42","unstructured":"(2021, September 27). PyTorch. Available online: https:\/\/pytorch.org\/."},{"key":"ref_43","unstructured":"(2021, September 27). Home-OpenCV. Available online: https:\/\/opencv.org\/."},{"key":"ref_44","unstructured":"(2021, June 30). Research\/object_detection at master \u00b7 tensorflow\/models. Available online: https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/object_detection\/g3doc\/tf2.md."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"012024","DOI":"10.1088\/1757-899X\/844\/1\/012024","article-title":"A review: Comparison of performance metrics of pretrained models for object detection using the TensorFlow framework","volume":"844","author":"Sanchez","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7346\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T05:24:51Z","timestamp":1721539491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,4]]},"references-count":45,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217346"],"URL":"https:\/\/doi.org\/10.3390\/s21217346","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,4]]}}}