{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T23:17:44Z","timestamp":1769555864852,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T00:00:00Z","timestamp":1647043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea government (MSIT)","award":["2020-0-00107"],"award-info":[{"award-number":["2020-0-00107"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The autonomous flight of an unmanned aerial vehicle refers to creating a new flight route after self-recognition and judgment when an unexpected situation occurs during the flight. The unmanned aerial vehicle can fly at a high speed of more than 60 km\/h, so obstacle recognition and avoidance must be implemented in real-time. In this paper, we propose to recognize objects quickly and accurately by effectively using the H\/W resources of small computers mounted on industrial unmanned air vehicles. Since the number of pixels in the image decreases after the resizing process, filtering and object resizing were performed according to the altitude, so that quick detection and avoidance could be performed. To this end, objects up to 60 m in height were classified by subdividing them at 20 m intervals, and objects unnecessary for object detection were filtered with deep learning methods. In the 40 m to 60 m sections, the average speed of recognition was increased by 38%, without compromising the accuracy of object detection.<\/jats:p>","DOI":"10.3390\/rs14061378","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T21:44:17Z","timestamp":1647207857000},"page":"1378","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep-Learning-Based Object Filtering According to Altitude for Improvement of Obstacle Recognition during Autonomous Flight"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5557-9660","authenticated-orcid":false,"given":"Yongwoo","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5455-0487","authenticated-orcid":false,"given":"Junkang","family":"An","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8435-0395","authenticated-orcid":false,"given":"Inwhee","family":"Joe","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.imavis.2010.03.009","article-title":"Combining monoSLAM with object recognition for scene augmentation using a wearable camera","volume":"28","author":"Castle","year":"2010","journal-title":"Image Vis. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Naikal, N., Yang, A.Y., and Sastry, S.S. (2010, January 26\u201329). Towards an efficient distributed object recognition system in wireless smart camera networks. Proceedings of the 2010 13th International Conference on Information Fusion, Edinburgh, UK.","DOI":"10.1109\/ICIF.2010.5711893"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yang, A.Y., Maji, S., Christoudias, C.M., Darrell, T., Malik, J., and Sastry, S.S. (September, January 30). Multiple-view object recognition in band-limited distributed camera networks. Proceedings of the 2009 Third ACM\/IEEE International Conference on Distributed Smart Cameras (ICDSC), Como, Italy.","DOI":"10.1109\/ICDSC.2009.5289410"},{"key":"ref_4","unstructured":"Ren, Z., Meng, J., and Yuan, J. (2011, January 13\u201316). Depth camera based hand gesture recognition and its applications in Human-Computer-Interaction. Proceedings of the 2011 8th International Conference on Information, Communications Signal Processing, Singapore."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xia, L., and Aggarwal, J. (2013, January 23\u201328). Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.365"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tham, J.S., Chang, Y.C., Fauzi, M.F.A., and Gwak, J. (2015, January 29\u201331). Object recognition using depth information of a consumer depth camera. Proceedings of the 2015 International Conference on Control, Automation and Information Sciences (ICCAIS), Changshu, China.","DOI":"10.1109\/ICCAIS.2015.7338662"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Melotti, G., Premebida, C., and Gon\u00e7alves, N. (2020, January 15\u201317). Multimodal deep-learning for object recognition combining camera and LIDAR data. Proceedings of the 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Ponta Delgada, Portugal.","DOI":"10.1109\/ICARSC49921.2020.9096138"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"623401","DOI":"10.1117\/12.666235","article-title":"Rapid and scalable 3D object recognition using LIDAR data","volume":"Volume 6234","author":"Matei","year":"2006","journal-title":"Proceedings of the Automatic Target Recognition XVI"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/70.134277","article-title":"An ultrasonic visual sensor for three-dimensional object recognition using neural networks","volume":"8","author":"Watanabe","year":"1992","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.ins.2014.08.021","article-title":"Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis","volume":"314","author":"Huang","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Heuer, H., and Breiter, A. (2020, January 12\u201318). More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, Genoa, Italy.","DOI":"10.1145\/3340631.3394873"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104046","DOI":"10.1016\/j.imavis.2020.104046","article-title":"Deep learning-based object detection in low-altitude UAV datasets: A survey","volume":"104","author":"Mittal","year":"2020","journal-title":"Image Vis. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lu, X., Kang, X., Nishide, S., and Ren, F. (2019, January 19\u201321). Object detection based on SSD-ResNet. Proceedings of the 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS), Singapore.","DOI":"10.1109\/CCIS48116.2019.9073753"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ali, H., Khursheed, M., Fatima, S.K., Shuja, S.M., and Noor, S. (2019, January 9\u201310). Object recognition for dental instruments using SSD-MobileNet. Proceedings of the 2019 International Conference on Information Science and Communication Technology (ICISCT), Karachi, Pakistan.","DOI":"10.1109\/CISCT.2019.8777441"},{"key":"ref_15","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5390","DOI":"10.15376\/biores.16.3.5390-5406","article-title":"Accurate and Automated Detection of Surface Knots on Sawn Timbers Using YOLO-V5 Model","volume":"16","author":"Fang","year":"2021","journal-title":"BioResources"},{"key":"ref_17","unstructured":"Zhang, C., Han, Z., Fu, H., Zhou, J.T., and Hu, Q. (2022, March 06). CPM-Nets: Cross Partial Multi-View Networks. Available online: https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/11b9842e0a271ff252c1903e7132cd68-Abstract.html."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s41074-017-0027-2","article-title":"Visual SLAM algorithms: A survey from 2010 to 2016","volume":"9","author":"Taketomi","year":"2017","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","article-title":"Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras","volume":"33","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5015","DOI":"10.1109\/JIOT.2018.2867917","article-title":"Analyzing and enhancing the security of ultrasonic sensors for autonomous vehicles","volume":"5","author":"Xu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"012064","DOI":"10.1088\/1757-899X\/152\/1\/012064","article-title":"Obstacle-avoiding robot with IR and PIR motion sensors","volume":"Volume 152","author":"Ismail","year":"2016","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Walambe, R., Marathe, A., and Kotecha, K. (2021). Multiscale object detection from drone imagery using ensemble transfer learning. Drones, 5.","DOI":"10.3390\/drones5030066"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107578","DOI":"10.1016\/j.patcog.2020.107578","article-title":"DPNet: Detail-preserving network for high quality monocular depth estimation","volume":"109","author":"Ye","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_24","unstructured":"Wang, Y., and Solomon, J.M. (2019). Prnet: Self-supervised learning for partial-to-partial registration. arXiv."},{"key":"ref_25","unstructured":"Joseph Nelson, J.S. (2020, June 10). YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS. Available online: https:\/\/blog.roboflow.com\/yolov5-is-here\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1378\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:35:27Z","timestamp":1760135727000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,12]]},"references-count":25,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061378"],"URL":"https:\/\/doi.org\/10.3390\/rs14061378","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,12]]}}}