{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T16:25:25Z","timestamp":1764433525050,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031366154"},{"type":"electronic","value":"9783031366161"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36616-1_39","type":"book-chapter","created":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T18:03:41Z","timestamp":1687629821000},"page":"490-504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Object Detection for\u00a0Rescue Operations by\u00a0High-Altitude Infrared Thermal Imaging Collected by\u00a0Unmanned Aerial Vehicles"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7650-5185","authenticated-orcid":false,"given":"Andrii","family":"Polukhin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2682-4668","authenticated-orcid":false,"given":"Yuri","family":"Gordienko","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2237-0187","authenticated-orcid":false,"given":"Gert","family":"Jervan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5478-0450","authenticated-orcid":false,"given":"Sergii","family":"Stirenko","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,25]]},"reference":[{"issue":"7","key":"39_CR1","doi-asserted-by":"publisher","first-page":"15717","DOI":"10.3390\/s150715717","volume":"15","author":"P Boccardo","year":"2015","unstructured":"Boccardo, P., Chiabrando, F., Dutto, F., Tonolo, F., Lingua, A.: UAV deployment exercise for mapping purposes: evaluation of emergency response applications. Sensors 15(7), 15717\u201315737 (2015)","journal-title":"Sensors"},{"issue":"3","key":"39_CR2","doi-asserted-by":"publisher","first-page":"285","DOI":"10.3390\/rs10020285","volume":"10","author":"A de Castro","year":"2018","unstructured":"de Castro, A., Torres-S\u00e1nchez, J., Pe\u00f1a, J., Jim\u00e9nez-Brenes, F., Csillik, O., L\u00f3pez-Granados, F.: An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens. 10(3), 285 (2018)","journal-title":"Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Kanistras, K., Martins, G., Rutherford, M.J., Valavanis, K.P.: A survey of unmanned aerial vehicles (UAVs) for traffic monitoring. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 221\u2013234 (2013)","key":"39_CR3","DOI":"10.1109\/ICUAS.2013.6564694"},{"doi-asserted-by":"crossref","unstructured":"Avola, D., Foresti, G.L., Martinel, N., Micheloni, C., Pannone, D., Piciarelli, C.: Aerial video surveillance system for small-scale UAV environment monitoring. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20136 (2017)","key":"39_CR4","DOI":"10.1109\/AVSS.2017.8078523"},{"issue":"5","key":"39_CR5","doi-asserted-by":"publisher","first-page":"5723","DOI":"10.1109\/TVT.2020.2982508","volume":"69","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Shi, L., Sun, L., Li, J., Ding, M., Shu, F.S.: Path planning for UAV-mounted mobile edge computing with deep reinforcement learning. IEEE Trans. Veh. Technol. 69(5), 5723\u20135728 (2020)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"39_CR6","doi-asserted-by":"publisher","first-page":"58322","DOI":"10.1109\/ACCESS.2020.2982411","volume":"8","author":"F Wang","year":"2020","unstructured":"Wang, F., Zhang, M., Wang, X., Ma, X., Liu, J.: Deep learning for edge computing applications: a state-of-the-art survey. IEEE Access 8, 58322\u201358336 (2020)","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Suo, J., Wang, T., Zhang, X., Chen, H., Zhou, W., Shi, W.: HIT-UAV: a high-altitude infrared thermal dataset for unmanned aerial vehicles (2022)","key":"39_CR7","DOI":"10.1038\/s41597-023-02066-6"},{"doi-asserted-by":"crossref","unstructured":"Shamsoshoara, A.: The FLAME dataset: aerial Imagery Pile burn detection using drones (UAVs) (2020)","key":"39_CR8","DOI":"10.1016\/j.comnet.2021.108001"},{"issue":"3","key":"39_CR9","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/TMM.2019.2932615","volume":"22","author":"Q Liu","year":"2020","unstructured":"Liu, Q., He, Z., Li, X., Zheng, Y.: PTB-TIR: a thermal infrared pedestrian tracking benchmark. IEEE Trans. Multimedia 22(3), 666\u2013675 (2020)","journal-title":"IEEE Trans. Multimedia"},{"doi-asserted-by":"crossref","unstructured":"Bondi, E., et al.: BIRDSAI: a dataset for detection and tracking in aerial thermal infrared videos. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1736\u20131745 (2020)","key":"39_CR10","DOI":"10.1109\/WACV45572.2020.9093284"},{"doi-asserted-by":"crossref","unstructured":"Beyerer, J., Ruf, M., Herrmann, C.: CNN-based thermal infrared person detection by domain adaptation. In: Dudzik, M.C., Ricklin, J.C. (eds.) Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything,Orlando, USA, p. 8. SPIE (2018)","key":"39_CR11","DOI":"10.1117\/12.2304400"},{"doi-asserted-by":"crossref","unstructured":"Levin, E., Zarnowski, A., McCarty, J.L., Bialas, J., Banaszek, A., Banaszek, S.: Feasibility study of inexpensive thermal sensors and small UAS deployment for living human detection in rescue missions application scenarios. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. XLI-B8, 99\u2013103 (2016)","key":"39_CR12","DOI":"10.5194\/isprs-archives-XLI-B8-99-2016"},{"doi-asserted-by":"crossref","unstructured":"Gordienko, Y., et al.: Scaling analysis of specialized tensor processing architectures for deep learning models. Deep Learn. Concepts Archit. 65\u201399 (2020)","key":"39_CR13","DOI":"10.1007\/978-3-030-31756-0_3"},{"doi-asserted-by":"crossref","unstructured":"Gordienko, Y., et al.: \u201cLast mile\u201d optimization of edge computing ecosystem with deep learning models and specialized tensor processing architectures. In: Advances in computers, vol. 122, pp. 303\u2013341. Elsevier (2021)","key":"39_CR14","DOI":"10.1016\/bs.adcom.2020.10.003"},{"key":"39_CR15","series-title":"Lecture Notes on Data Engineering and Communications Technologies","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-031-04809-8_6","volume-title":"Advances in Artificial Systems for Logistics Engineering","author":"V Taran","year":"2022","unstructured":"Taran, V., Gordienko, Y., Rokovyi, O., Alienin, O., Kochura, Y., Stirenko, S.: Edge intelligence for medical applications under field conditions. In: Hu, Z., Zhang, Q., Petoukhov, S., He, M. (eds.) ICAILE 2022. LNDECT, vol. 135, pp. 71\u201380. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-04809-8_6"},{"key":"39_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361 (2012)","key":"39_CR17","DOI":"10.1109\/CVPR.2012.6248074"},{"issue":"2","key":"39_CR18","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"},{"key":"39_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.comcom.2019.10.007","volume":"149","author":"S Sudhakar","year":"2020","unstructured":"Sudhakar, S., Vijayakumar, V., Kumar, C.S., Priya, V., Ravi, L., Subramaniyaswamy, V.: Unmanned aerial vehicle (UAV) based forest fire detection and monitoring for reducing false alarms in forest-fires. Comput. Commun. 149, 1\u201316 (2020)","journal-title":"Comput. Commun."},{"unstructured":"Bendea, H., Boccardo, P., Dequal, S., Tonolo, F.G., Marenchino, D., Piras, M.: Low cost UAV for post-disaster assessment. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 37, 1373-1379 (2008)","key":"39_CR20"},{"issue":"9","key":"39_CR21","first-page":"4052","volume":"64","author":"John Gunnar Carlsson and Siyuan Song","year":"2018","unstructured":"John Gunnar Carlsson and Siyuan Song: Coordinated logistics with a truck and a drone. Manage. Sci. 64(9), 4052\u20134069 (2018)","journal-title":"Manage. Sci."},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25. Curran Associates Inc. (2012)","key":"39_CR22"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)","key":"39_CR23"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv:1512.03385 [cs], p. 12 (2015)","key":"39_CR24","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 [cs], p. 9 (2017)","key":"39_CR25"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440\u20131448 (2015)","key":"39_CR26","DOI":"10.1109\/ICCV.2015.169"},{"unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv:1506.01497 [cs], p. 14 (2016)","key":"39_CR27"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, p. 10. IEEE (2016)","key":"39_CR28","DOI":"10.1109\/CVPR.2016.91"},{"unstructured":"Liu, W., et al.: SSD: single shot multibox detector. arXiv:1512.02325 [cs], 9905:17 (2016)","key":"39_CR29"},{"key":"39_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: optimal speed and accuracy of object detection (2020)","key":"39_CR31"},{"doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation (2018)","key":"39_CR32","DOI":"10.1109\/CVPR.2018.00913"},{"key":"39_CR33","doi-asserted-by":"publisher","first-page":"141861","DOI":"10.1109\/ACCESS.2021.3120870","volume":"9","author":"S Li","year":"2021","unstructured":"Li, S., Li, Y., Li, Y., Li, M., Xiaorong, X.: YOLO-FIRI: improved YOLOv5 for infrared image object detection. IEEE Access 9, 141861\u2013141875 (2021)","journal-title":"IEEE Access"},{"key":"39_CR34","first-page":"9","volume":"2","author":"Z Kun","year":"2004","unstructured":"Kun, Z.: Background noise suppression in small targets infrared images and its method discussion. Opt. Optoelectron. Technol. 2, 9\u201312 (2004)","journal-title":"Opt. Optoelectron. Technol."},{"doi-asserted-by":"crossref","unstructured":"Anju, T.S., Nelwin Raj, N.R.: Shearlet transform based image denoising using histogram thresholding. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 162\u2013166 (2016)","key":"39_CR35","DOI":"10.1109\/CSN.2016.7824007"},{"issue":"2","key":"39_CR36","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"P Viola","year":"2004","unstructured":"Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137\u2013154 (2004)","journal-title":"Int. J. Comput. Vision"},{"doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886\u2013893 (2005)","key":"39_CR37","DOI":"10.1109\/CVPR.2005.177"},{"unstructured":"Jocher, G., et al.: Ultralytics\/YOLOv5: V7.0 - YOLOv5 SOTA realtime instance segmentation (2022)","key":"39_CR38"},{"doi-asserted-by":"crossref","unstructured":"Taran, V., et al.: Performance evaluation of deep learning networks for semantic segmentation of traffic stereo-pair images. In: Proceedings of the 19th International Conference on Computer Systems and Technologies, pp. 73\u201380 (2018)","key":"39_CR39","DOI":"10.1145\/3274005.3274032"},{"key":"39_CR40","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-3-030-16621-2_17","volume-title":"Advances in Computer Science for Engineering and Education II","author":"V Taran","year":"2020","unstructured":"Taran, V., Gordienko, Y., Rokovyi, A., Alienin, O., Stirenko, S.: Impact of ground truth annotation quality on performance of semantic image segmentation of traffic conditions. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2019. AISC, vol. 938, pp. 183\u2013193. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-16621-2_17"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36616-1_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T23:47:40Z","timestamp":1729640860000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36616-1_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031366154","9783031366161"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36616-1_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"25 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IbPRIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Conference on Pattern Recognition and Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Alicante","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibpria2022b","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"86","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}