{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T04:25:01Z","timestamp":1783916701365,"version":"3.55.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031942624","type":"print"},{"value":"9783031942631","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-94263-1_3","type":"book-chapter","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T19:14:42Z","timestamp":1748805282000},"page":"36-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TRYOLO: A Transformer-Based Real-Time Object Detection Model for\u00a0UAV Images"],"prefix":"10.1007","author":[{"given":"Bhimendra","family":"Dewangan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Srinivas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. B. V.","family":"Subramanyam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Malagi, V.P., DR, R.B., Rangarajan, K.: Multi-object tracking in aerial image sequences using aerial tracking learning and detection algorithm. Defence Sci. J. 66(2), 122\u2013129 (2016)","DOI":"10.14429\/dsj.66.8972"},{"issue":"4","key":"3_CR2","doi-asserted-by":"publisher","first-page":"2152","DOI":"10.1109\/TIP.2011.2172798","volume":"21","author":"H-Y Cheng","year":"2011","unstructured":"Cheng, H.-Y., Weng, C.-C., Chen, Y.-Y.: Vehicle detection in aerial surveillance using dynamic Bayesian networks. IEEE Trans. Image Process. 21(4), 2152\u20132159 (2011)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"3_CR3","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/s44196-023-00302-w","volume":"16","author":"U Sirisha","year":"2023","unstructured":"Sirisha, U., Praveen, S.P., Srinivasu, P.N., et al.: Statistical analysis of design aspects of various YOLO-based deep learning models for object detection. Int. J. Comput. Intell. Syst. 16(1), 126 (2023)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"3_CR4","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., et al.: YOLOv10: Real-Time End-to-End Object Detection, arXiv preprint arXiv:2405.14458 (2024)"},{"key":"3_CR5","unstructured":"Khanam, R., Hussain, M.: YOLOv11: An Overview of the Key Architectural Enhancements, arXiv preprint arXiv:2410.17725 (2024)"},{"issue":"4","key":"3_CR6","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.sigpro.2010.08.010","volume":"91","author":"Y Pang","year":"2011","unstructured":"Pang, Y., Yuan, Y., Li, X., Pan, J.: Efficient HOG human detection. Signal Process. 91(4), 773\u2013781 (2011)","journal-title":"Signal Process."},{"key":"3_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108548","volume":"126","author":"V Chalavadi","year":"2022","unstructured":"Chalavadi, V., Jeripothula, P., Datla, R., Ch, S.B., et al.: mSODANet: a network for multi-scale object detection in aerial images using hierarchical dilated convolutions. Pattern Recognit. 126, 108548 (2022)","journal-title":"Pattern Recognit."},{"key":"3_CR8","unstructured":"Jocher, G., Stoken, A., Borovec, J., et al.: ultralytics\/yolov5: v5.0-YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations, Zenodo (2021)"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1222\u20131230 (2017)","DOI":"10.1109\/CVPR.2017.211"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Yang, F., Fan, H., Chu, P., Blasch, E., Ling, H.: Clustered object detection in aerial images. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 8311\u20138320 (2019)","DOI":"10.1109\/ICCV.2019.00840"},{"key":"3_CR11","unstructured":"Zheng, G., Liu, S., Feng, W., Li, Z., Sun, J., et al.: YOLOX: Exceeding YOLO series in 2021, arXiv preprint arXiv:2107.08430 (2021)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3329687","volume":"20","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Wu, C., Zhang, T., Liu, Y., Zheng, Y.: Self-attention guidance and multiscale feature fusion-based UAV image object detection. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Koonce, B.: Convolutional Neural Networks with Swift for Tensorflow. Springer (2021)","DOI":"10.1007\/978-1-4842-6168-2"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"David, E., Madec, S., et al.: Global wheat head detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods. Plant Phenomics (2020)","DOI":"10.34133\/2020\/3521852"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information, arXiv preprint arXiv:2402.13616 (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"3_CR17","unstructured":"Reis, D., Kupec, J., Hong, J., Daoudi, A.: Real-Time Flying Object Detection with YOLOv8, arXiv preprint arXiv:2305.09972 (2024)"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Srinivas, M., Lin, Y.Y., Liao, H.Y.M.: Learning deep and sparse feature representation for fine-grained object recognition. In: Proceedings of IEEE International Conference on Multimedia Expo (ICME), pp. 1458\u20131463 (2017)","DOI":"10.1109\/ICME.2017.8019386"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Srinivas, M., Roy, D., Mohan, C.K.: Discriminative feature extraction from X-ray images using deep convolutional neural networks. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 917\u2013921 (2016)","DOI":"10.1109\/ICASSP.2016.7471809"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 56, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Liu, W.: SSD: single shot multibox detector. In: Proceedings of European Conference on Computer Vision, pp. 21\u201337. Springer, Cham (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"3_CR24","unstructured":"Farhadi, A., Redmon, J.: YOLOv3: an incremental improvement, arXiv preprint arXiv:1804.02767 (2018)"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Z.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3_CR26","unstructured":"Du, D., Zhu, P., Wen, L., Bian, X., et al.: VisDrone-DET2019: the vision meets drone object detection in image challenge results. In: Proceedings of IEEE\/CVF International Conference on Computer Vision Workshops, pp. 213\u2013126 (2019)"}],"container-title":["Communications in Computer and Information Science","Innovations for Community Services"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-94263-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T19:14:47Z","timestamp":1748805287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94263-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031942624","9783031942631"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94263-1_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"I4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Innovations for Community Services","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"i4cs2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.i4cs-conference.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}