{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:26:11Z","timestamp":1784258771525,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provide is extremely valuable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by the on-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases, UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the fly path task for UAVs, considering the actual perception needs. A convolutional neural networks (CNNs) model is proposed to detect and localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accurately recognize as many objects as possible with a minimum time cost. The simulation results demonstrate that the proposed method is effective and efficient, and can address the actual scene understanding and path planning problems for UAVs in the real world well.<\/jats:p>","DOI":"10.3390\/s22030891","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T21:07:11Z","timestamp":1643144831000},"page":"891","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8131-5009","authenticated-orcid":false,"given":"Songhe","family":"Yuan","sequence":"first","affiliation":[{"name":"Institute of Advanced Manufacturing Technology, HeFei Institutes of Physical Science, Chinese Academy of Sciences, Changzhou 213164, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaoru","family":"Ota","sequence":"additional","affiliation":[{"name":"Department of Information and Electronic Engineering, Muroran Institute of Technology, Muroran 050-8585, Hokkaido, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mianxiong","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Information and Electronic Engineering, Muroran Institute of Technology, Muroran 050-8585, Hokkaido, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghai","family":"Zhao","sequence":"additional","affiliation":[{"name":"HeFei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1007\/s11227-014-1161-6","article-title":"UAV-assisted data gathering in wireless sensor networks","volume":"70","author":"Dong","year":"2014","journal-title":"J. Supercomput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3458","DOI":"10.1109\/TPDS.2017.2740294","article-title":"Eyes in the Dark: Distributed Scene Understanding for Disaster Management","volume":"28","author":"Li","year":"2017","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, F., Bai, Z., Xun, F., Zhang, Y., and Zhao, X. (2021). Deep learning-based congestion detection at urban intersections. Sensors, 21.","DOI":"10.3390\/s21062052"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106331","DOI":"10.1016\/j.cmpb.2021.106331","article-title":"Automatic creation of annotations for chest radiographs based on the positional information extracted from radiographic image reports","volume":"209","author":"Wang","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Maldonado, J., and Giefer, L.A. (2021). A comparison of bottom-up models for spatial saliency predictions in autonomous driving. Sensors, 21.","DOI":"10.3390\/s21206825"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MNET.2018.1700202","article-title":"Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing","volume":"32","author":"Li","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/TPAMI.2015.2437377","article-title":"Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding","volume":"38","author":"Mottaghi","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/TPAMI.2013.185","article-title":"3D Traffic Scene Understanding From Movable Platforms","volume":"36","author":"Geiger","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1109\/TFUZZ.2014.2298233","article-title":"A Scene Image is Nonmutually Exclusive? A Fuzzy Qualitative Scene Understanding","volume":"22","author":"Lim","year":"2014","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1109\/TSMC.2015.2491878","article-title":"Vision-Based Target Detection and Localization via a Team of Cooperative UAV and UGVs","volume":"46","author":"Minaeian","year":"2016","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"162841","DOI":"10.1109\/ACCESS.2019.2951294","article-title":"Urban commerce distribution analysis based on street view and deep learning","volume":"7","author":"Ye","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6778","DOI":"10.1109\/JSEN.2017.2746184","article-title":"A Novel Trail Detection and Scene Understanding Framework for a Quadrotor UAV with Monocular Vision","volume":"17","author":"Liu","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/TVCG.2015.2417575","article-title":"The Clutterpalette: An Interactive Tool for Detailing Indoor Scenes","volume":"22","author":"Yu","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1109\/JSEN.2018.2874665","article-title":"Intelligent Driving Data Recorder in Smartphone Using Deep Neural Network-Based Speedometer and Scene Understanding","volume":"19","author":"Gu","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1109\/TITS.2017.2702012","article-title":"Cross-Domain Traffic Scene Understanding: A Dense Correspondence-Based Transfer Learning Approach","volume":"19","author":"Di","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/TPAMI.2014.2366143","article-title":"Adopting Abstract Images for Semantic Scene Understanding","volume":"38","author":"Zitnick","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8052","DOI":"10.1109\/TIE.2018.2807401","article-title":"Vision-Based Target Three-Dimensional Geolocation Using Unmanned Aerial Vehicles","volume":"65","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4968","DOI":"10.1109\/JSTARS.2018.2879368","article-title":"Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network","volume":"11","author":"Zhu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/JSTARS.2018.2793849","article-title":"Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks","volume":"11","author":"Fan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X., Chen, S., Song, L., Wo\u017aniak, M., and Liu, S. (2021). Self-attention negative feedback network for real-time image super-resolution. J. King Saud-Univ.-Comput. Inf. Sci., in press.","DOI":"10.1016\/j.jksuci.2021.07.014"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Subramani, P., Sattar, K.N.A., de Prado, R.P., Girirajan, B., and Wozniak, M. (2021). Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles. Appl. Sci., 11.","DOI":"10.3390\/app11177984"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/TMECH.2014.2301459","article-title":"Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles","volume":"20","author":"Yu","year":"2015","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, Z., and Zhao, L. (2017, January 26\u201327). The Comparison of Four UAV Path Planning Algorithms Based on Geometry Search Algorithm. Proceedings of the 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.","DOI":"10.1109\/IHMSC.2017.123"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Morita, T., Oyama, K., Mikoshi, T., and Nishizono, T. (2018, January 23\u201327). Decision Making Support of UAV Path Planning for Efficient Sensing in Radiation Dose Mapping. Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan.","DOI":"10.1109\/COMPSAC.2018.00053"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/JAS.2015.7081657","article-title":"UAV online path planning algorithm in a low altitude dangerous environment","volume":"2","author":"Wen","year":"2015","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1109\/JIOT.2017.2717078","article-title":"Offline and Online Search: UAV Multiobjective Path Planning Under Dynamic Urban Environment","volume":"5","author":"Yin","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Imran, M.A., Onireti, O., Ansari, S., and Abbasi, Q.H. (2021). Autonomous Airborne Wireless Networks, Wiley-IEEE Press.","DOI":"10.1002\/9781119751717"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Just, G.E., E Pellenz, M., Lima, L.A., S Chang, B., Demo Souza, R., and Montejo-S\u00e1nchez, S. (2020). UAV Path Optimization for Precision Agriculture Wireless Sensor Networks. Sensors, 20.","DOI":"10.3390\/s20216098"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, C., Liu, P., Zhang, T., and Sun, J. (2018, January 12\u201314). The Adaptive Vortex Search Algorithm of Optimal Path Planning for Forest Fire Rescue UAV. Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC.2018.8577733"},{"key":"ref_30","unstructured":"Lifen, L., Ruoxin, S., Shuandao, L., and Jiang, W. (2016, January 12\u201314). Path planning for UAVS based on improved artificial potential field method through changing the repulsive potential function. Proceedings of the 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), Nanjing, China."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, J., Li, J., and Wang, X. (2017, January 28\u201330). UAV path planning based on receding horizon control with adaptive strategy. Proceedings of the 2017 29th Chinese Control And Decision Conference (CCDC), Chongqing, China.","DOI":"10.1109\/CCDC.2017.7978637"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1109\/JIOT.2018.2796243","article-title":"MVO-Based 2-D Path Planning Scheme for Providing Quality of Service in UAV Environment","volume":"5","author":"Kumar","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, J., Ye, F., and Jiang, T. (2017, January 27\u201330). Path planning under obstacle-avoidance constraints based on ant colony optimization algorithm. Proceedings of the 2017 IEEE 17th International Conference on Communication Technology (ICCT), Chengdu, China.","DOI":"10.1109\/ICCT.2017.8359869"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhou, W., and Zhang, Y. (2016, January 12\u201314). On collaborative path planning for multiple UAVs based on Pythagorean Hodograph curve. Proceedings of the 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), Nanjing, China.","DOI":"10.1109\/CGNCC.2016.7828917"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TRO.2015.2459812","article-title":"Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints","volume":"31","author":"Yang","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lo, L.Y., Yiu, C.H., Tang, Y., Yang, A.S., Li, B., and Wen, C.Y. (2021). Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications. Sensors, 21.","DOI":"10.3390\/s21237888"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz, J., L\u00f3pez, B., Quevedo, F., Monje, C.A., Garrido, S., and Moreno, L.E. (2021). Multi UAV Coverage Path Planning in Urban Environments. Sensors, 21.","DOI":"10.3390\/s21217365"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Melo, A.G., Pinto, M.F., Marcato, A.L., Hon\u00f3rio, L.M., and Coelho, F.O. (2021). Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs. Sensors, 21.","DOI":"10.3390\/s21041108"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Santin, R., Assis, L., Vivas, A., and Pimenta, L.C. (2021). Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage. Sensors, 21.","DOI":"10.3390\/s21051705"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_44","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., and Girshick, R. (2020, December 13). Detectron2. Available online: https:\/\/github.com\/facebookresearch\/detectron2."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/891\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:06:47Z","timestamp":1760134007000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,24]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030891"],"URL":"https:\/\/doi.org\/10.3390\/s22030891","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,24]]}}}