{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:50:30Z","timestamp":1773247830027,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["2019R1F1A1058489"],"award-info":[{"award-number":["2019R1F1A1058489"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object detection methods have been applied in several aerial and traffic surveillance applications. However, object detection accuracy decreases in low-resolution (LR) images owing to feature loss. To address this problem, we propose a single network, SRODNet, that incorporates both super-resolution (SR) and object detection (OD). First, a modified residual block (MRB) is proposed in the SR to recover the feature information of LR images, and this network was jointly optimized with YOLOv5 to benefit from hierarchical features for small object detection. Moreover, the proposed model focuses on minimizing the computational cost of network optimization. We evaluated the proposed model using standard datasets such as VEDAI-VISIBLE, VEDAI-IR, DOTA, and Korean highway traffic (KoHT), both quantitatively and qualitatively. The experimental results show that the proposed method improves the accuracy of vehicular detection better than other conventional methods.<\/jats:p>","DOI":"10.3390\/rs14246270","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T04:34:20Z","timestamp":1670819660000},"page":"6270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["SRODNet: Object Detection Network Based on Super Resolution for Autonomous Vehicles"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8087-4952","authenticated-orcid":false,"given":"Yogendra Rao","family":"Musunuri","sequence":"first","affiliation":[{"name":"Department of Control and Instrumentation Engineering, Changwon National University, Changwon 51140, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1077-9615","authenticated-orcid":false,"given":"Oh-Seol","family":"Kwon","sequence":"additional","affiliation":[{"name":"School of Electrical, Electronics and Control Engineering, Changwon National University, Changwon 51140, Republic of Korea"}]},{"given":"Sun-Yuan","family":"Kung","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1109\/TIFS.2018.2812196","article-title":"Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery","volume":"13","author":"Akcay","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s00138-015-0706-x","article-title":"Multi-view object detection in dual-energy X-ray images","volume":"26","author":"Bastan","year":"2015","journal-title":"Mach. Vis. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/TSMC.2016.2628381","article-title":"Modern Computer Vision Techniques for X-ray Testing in Baggage Inspection","volume":"47","author":"Mery","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"120043","DOI":"10.1109\/ACCESS.2021.3107975","article-title":"Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines","volume":"9","author":"Choi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shi, X., Li, X., Wu, C., Kong, S., Yang, J., and He, L. (2020, January 4\u20138). A Real-Time Deep Network for Crowd Counting. Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053780"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, P., Adnan, K., Lyu, X., Wei, S., and Sinnott, R. (2020, January 16\u201318). Estimating the Size of Crowds through Deep Learning. Proceedings of the 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia.","DOI":"10.1109\/CSDE50874.2020.9411377"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5495","DOI":"10.1007\/s11042-020-09964-6","article-title":"A deep learning approach to building an intelligent video surveillance system","volume":"80","author":"Xu","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent Advances in Deep Learning for Object Detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.3390\/electronics10101140","article-title":"A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment","volume":"10","author":"Mingyu","year":"2021","journal-title":"Electronics"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kulshreshtha, M., Chandra, S.S., Randhawa, P., Tsaramirsis, G., Khadidos, A., and Khadidos, A. (2021). OATCR: Outdoor Autonomous Trash-Collecting Robot Design Using YOLOv4-Tiny. Electronics, 10.","DOI":"10.3390\/electronics10182292"},{"key":"ref_11","first-page":"282","article-title":"Machine vision for robotics","volume":"30","author":"Nelson","year":"1983","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Loukatos, D., Petrongonas, E., Manes, K., Kyrtopoulos, I.-V., Dimou, V., and Arvanitis, K.G. (2021). A Synergy of Innovative Technologies towards Implementing an Autonomous DIY Electric Vehicle for Harvester-Assisting Purposes. Machines, 9.","DOI":"10.3390\/machines9040082"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/vehicles4010016","article-title":"Autonomous Human-Vehicle Leader-Follower Control Using Deep-Learning-Driven Gesture Recognition","volume":"4","author":"Schulte","year":"2022","journal-title":"Vehicles"},{"key":"ref_14","first-page":"1635","article-title":"Automatic Car Counting Method for Unmanned Aerial Vehicle Image","volume":"3","author":"Thomas","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/LGRS.2015.2439517","article-title":"Fast multi-class vehicle detection on aerial images","volume":"12","author":"Liu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.3390\/rs14092088","article-title":"Rapid Vehicle Detection in Aerial Images under the Complex Background of Dense Urban Areas","volume":"14","author":"Shengjie","year":"2022","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"549","DOI":"10.3390\/ijgi10080549","article-title":"Vehicle Detection in Very-High-Resolution Remote Sensing Images Based on an Anchor-Free Detection Model with a More Precise Foveal Area","volume":"10","author":"Xungen","year":"2021","journal-title":"Int. J. Geo-Inf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2720","DOI":"10.3390\/s17122720","article-title":"Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks","volume":"17","author":"Jiandan","year":"2017","journal-title":"Sensors"},{"key":"ref_19","first-page":"326","article-title":"A Progressive Review\u2014Emerging Technologies for ADAS Driven Solutions","volume":"1","author":"Jaswanth","year":"2021","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"94371","DOI":"10.1109\/ACCESS.2021.3093698","article-title":"Novel On-Road Vehicle Detection System Using Multi-Stage Convolutional Neural Network","volume":"9","author":"Kim","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","unstructured":"Kiho, L., and Kastuv, T. (2019, January 11\u201314). LIDAR: Lidar Information based Dynamic V2V Authentication for Roadside Infrastructure-less Vehicular Networks. Proceedings of the 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Aldrich, R., and Wickramarathne, T. (2018, January 3\u20136). Low-Cost Radar for Object Tracking in Autonomous Driving: A Data-Fusion Approach. Proceedings of the 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Porto, Portugal.","DOI":"10.1109\/VTCSpring.2018.8417751"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"78311","DOI":"10.1109\/ACCESS.2019.2922479","article-title":"Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.vlsi.2021.01.002","article-title":"Vulnerable objects detection for autonomous driving: A review","volume":"78","author":"Khatab","year":"2021","journal-title":"Integration"},{"key":"ref_25","first-page":"1","article-title":"A Deep Journey into Super-resolution: A Survey","volume":"53","author":"Saeed","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_26","unstructured":"Yogendra Rao, M., Arvind, M., and Oh-Seol, K. (2021, January 6\u20137). Single Image Super-Resolution Using Deep Residual Network with Spectral Normalization. Proceedings of the 17th International Conference on Multimedia Technology and Applications (MITA), Jeju, Republic of Korea."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"555","DOI":"10.3390\/electronics10050555","article-title":"Deep residual dense network for single image super-resolution","volume":"10","year":"2021","journal-title":"Electronics"},{"key":"ref_28","first-page":"e12930","article-title":"Improved detection of small objects in road network sequences using CNN and super resolution","volume":"39","author":"Ivan","year":"2021","journal-title":"Expert Syst."},{"key":"ref_29","first-page":"9999398","article-title":"Towards Efficient Video Detection Object Super-Resolution with Deep Fusion Network for Public Safety","volume":"1","author":"Sheng","year":"2021","journal-title":"Wiley"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"302","DOI":"10.3390\/electronics7110302","article-title":"Multi-Object Detection in Traffic Scenes Based on Improved SSD","volume":"7","author":"Xinqing","year":"2018","journal-title":"Electronics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3152","DOI":"10.3390\/rs12193152","article-title":"Small Object Detection in Remote Sensing Images Based on Super-Resolution with Auxiliary Generative Adversarial Networks","volume":"12","author":"Luc","year":"2020","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yunyan, W., Huaxuan, W., Luo, S., Chen, P., and Zhiwei, Y. (2022). Detection of plane in remote sensing images using super-resolution. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0265503"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"82306","DOI":"10.1109\/ACCESS.2020.2990870","article-title":"Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network","volume":"8","author":"Mostofa","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","unstructured":"Chao, D., Chen, C.L., and Xiaoou, T. (2016, January 8\u201316). Accelerating the Super-Resolution Convolutional Neural Network. Proceedings of the 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands."},{"key":"ref_36","unstructured":"Zhaowen, W., Ding, L., Jianchao, Y., Wei, H., and Thomas, H. (2015, January 7\u201313). Deep Networks for Image Super-Resolution with Sparse Prior. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile."},{"key":"ref_37","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u201312). Very deep convolutional networks for large-scale image recognition. Proceedings of the CVPR 2015, Boston, MA, USA."},{"key":"ref_38","unstructured":"Wei-Sheng, L., Jia-Bin, H., Narendra, A., and Ming-Hsuan, Y. (2017, January 21\u201326). Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_39","unstructured":"Bee, L., Sanghyun, S., Heewon, K., Seungjun, N., and Kyoung Mu, L. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the IEEE Conference on computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"892","DOI":"10.3390\/electronics8080892","article-title":"Multi-Scale Inception Based Super-Resolution Using Deep Learning Approach","volume":"8","author":"Wazir","year":"2019","journal-title":"Electronics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"339","DOI":"10.3390\/electronics8030339","article-title":"An efficient super-resolution network based on aggregated residual transformations","volume":"8","author":"Yan","year":"2019","journal-title":"Electronics"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_44","unstructured":"Zhiqian, C., Kai, C., and James, C. (2013, January 14\u201315). Vehicle and Pedestrian Detection Using Support Vector Machine and Histogram of Oriented Gradients Features. Proceedings of the 2013 International Conference on Computer Sciences and Applications, Wuhan, China."},{"key":"ref_45","unstructured":"Zahid, M., Nazeer, M., Arif, M., Imran, S., Fahad, K., Mazhar, A., Uzair, K., and Samee, K. (2016, January 19\u201321). Boosting the Accuracy of AdaBoost for Object Detection and Recognition. Proceedings of the 2016 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan."},{"key":"ref_46","unstructured":"Silva, R., Rodrigues, P., Giraldi, G., and Cunha, G. (2005, January 6\u20138). Object recognition and tracking using Bayesian networks for augmented reality systems. Proceedings of the Ninth International Conference on Information Visualization (IV\u201905), London, UK."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Qi, Z., Wang, L., Xu, Y., and Zhong, P. (2008, January 10\u201312). Robust Object Detection Based on Decision Trees and a New Cascade Architecture. Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation, Vienna, Austria.","DOI":"10.1109\/CIMCA.2008.108"},{"key":"ref_48","unstructured":"Fica Aida, N., Purwalaksana, A., and Manalu, I. (2019, January 9\u201310). Object Detection of Surgical Instruments for Assistant Robot Surgeon using KNN. Proceedings of the 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), Batu, Indonesia."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, Z., and Xiong, H. (2012, January 6\u20137). Object Detection and Localization Using Random Forest. Proceedings of the 2012 Second International Conference on Intelligent System Design and Engineering Application, Sanya, China.","DOI":"10.1109\/ISdea.2012.669"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2016, January 18). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girishick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_55","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_56","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Mark Liao, H.-Y. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Mark Liao, H.-Y. (2020). Scaled-YOLOv4: Scaling Cross Stage Partial Network. arXiv.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"ref_58","first-page":"4503613","article-title":"YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving","volume":"70","author":"Yingfeng","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lian, J., Yin, Y., Li, L., Wang, Z., and Zhou, Y. (2021). Small Object Detection in Traffic Scenes based on Attention Feature Fusion. Sensors, 21.","DOI":"10.3390\/s21093031"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.-H., Zhang, L., Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Ntire 2017 challenge on single image super-resolution: Methods and results. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_61","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., and Alberi-Morel, M.L. (2012, January 3\u20137). Low-complexity single-image super-resolution based on nonnegative neighbor embedding. Proceedings of the 23rd British Machine Vision Conference Location (BMVC), Guildford, UK."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Timofte, R., De Smet, V., and Van Gool, L. (2014, January 1\u20132). A+: Adjusted anchored neighborhood regression for fast super-resolution. Proceedings of the Asian Conference on Computer Vision (ACCV), Singapore.","DOI":"10.1109\/ICCV.2013.241"},{"key":"ref_63","unstructured":"Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001, January 7\u201314). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of the 8th international Conference of Computer Vision (ICCV), Vancouver, BC, Canada."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., and Ahuja, N. (2015, January 8\u201310). Single image super-resolution from transformed self-exemplars. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hor\u00e9, A., and Ziou, D. (2010, January 23\u201326). Image quality metrics: PSNR vs. SSIM. Proceedings of the 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_66","unstructured":"Venkatanath, N., Praneeth, D., Chandrasekhar, B.M., Channappayya, S.S., and Medasani, S.S. (March, January 27). Blind Image Quality Evaluation Using Perception Based Features. Proceedings of the 21st National Conference on Communications (NCC), Mumbai, India."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhong, J., and Tan, Y. (2019). Multiple-oriented and small object detection with convolutional neural networks for aerial image. Remote Sens., 11.","DOI":"10.3390\/rs11182176"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6270\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:58Z","timestamp":1760146678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,10]]},"references-count":67,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246270"],"URL":"https:\/\/doi.org\/10.3390\/rs14246270","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,10]]}}}