{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T22:36:54Z","timestamp":1784155014936,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["20101216855"],"award-info":[{"award-number":["20101216855"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["21-1-2-18-xx"],"award-info":[{"award-number":["21-1-2-18-xx"]}]},{"name":"Key R&amp;D Projects of Qingdao Science and Technology Plan","award":["20101216855"],"award-info":[{"award-number":["20101216855"]}]},{"name":"Key R&amp;D Projects of Qingdao Science and Technology Plan","award":["21-1-2-18-xx"],"award-info":[{"award-number":["21-1-2-18-xx"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a Fine-grained Target Focusing Network (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global\u2013Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.<\/jats:p>","DOI":"10.3390\/rs14163919","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:44:03Z","timestamp":1660607043000},"page":"3919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["FiFoNet: Fine-Grained Target Focusing Network for Object Detection in UAV Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Yue","family":"Xi","sequence":"first","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0940-3338","authenticated-orcid":false,"given":"Wenjing","family":"Jia","sequence":"additional","affiliation":[{"name":"Global Big Data Technologies Centre, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2872-388X","authenticated-orcid":false,"given":"Qiguang","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2751-6096","authenticated-orcid":false,"given":"Xiangzeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8945-3046","authenticated-orcid":false,"given":"Xiaochen","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanhui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Avola, D., Cinque, L., Diko, A., Fagioli, A., Foresti, G.L., Mecca, A., Pannone, D., and Piciarelli, C. (2021). MS-Faster R-CNN: Multi-stream backbone for improved Faster R-CNN object detection and aerial tracking from UAV images. Remote Sens., 13.","DOI":"10.3390\/rs13091670"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Stojni\u0107, V., Risojevi\u0107, V., Mu\u0161tra, M., Jovanovi\u0107, V., Filipi, J., Kezi\u0107, N., and Babi\u0107, Z. (2021). A method for detection of small moving objects in UAV videos. Remote Sens., 13.","DOI":"10.3390\/rs13040653"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ma, Y., Li, Q., Chu, L., Zhou, Y., and Xu, C. (2021). Real-time detection and spatial localization of insulators for UAV inspection based on binocular stereo vision. Remote Sens., 13.","DOI":"10.3390\/rs13020230"},{"key":"ref_4","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Paradise, NV, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2021, January 19\u201325). Scaled-yolov4: Scaling cross stage partial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"ref_6","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., and Ling, H. (2021). Detection and Tracking Meet Drones Challenge. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2021.3119563"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wen, L., Du, D., Zhu, P., Hu, Q., Wang, Q., Bo, L., and Lyu, S. (2021, January 19\u201325). Detection, tracking, and counting meets drones in crowds: A benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00772"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1109\/TIP.2020.3045636","article-title":"A global-local self-adaptive network for drone-view object detection","volume":"30","author":"Deng","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Liao, W., Yang, X., Tang, J., and He, T. (2022). Scrdet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2022.3166956"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (2019, January 16\u201320). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/TMM.2021.3074273","article-title":"Extended feature pyramid network for small object detection","volume":"24","author":"Deng","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_13","unstructured":"Noh, J., Bae, W., Lee, W., Seo, J., and Kim, G. (November, January 27). Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection. Proceedings of the the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bashir, S.M.A., and Wang, Y. (2021). Small object detection in remote sensing images with residual feature aggregation-based super-resolution and object detector network. Remote Sens., 13.","DOI":"10.3390\/rs13091854"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108199","DOI":"10.1016\/j.patcog.2021.108199","article-title":"Context-aware co-supervision for accurate object detection","volume":"121","author":"Peng","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tang, X., Du, D.K., He, Z., and Liu, J. (2018, January 8\u201314). Pyramidbox: A context-assisted single shot face detector. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_49"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107867","DOI":"10.1016\/j.patcog.2021.107867","article-title":"Spatial context-aware network for salient object detection","volume":"114","author":"Kong","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_19","unstructured":"Jiao, L., Gao, J., Liu, X., Liu, F., Yang, S., and Hou, B. (2021). Multi-Scale Representation Learning for Image Classification: A Survey. IEEE Trans. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qiao, S., Chen, L.C., and Yuille, A. (2021, January 19\u201325). Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., and Zhang, L. (2021, January 19\u201325). Dynamic head: Unifying object detection heads with attentions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1109\/TPAMI.2019.2933510","article-title":"P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization","volume":"44","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","first-page":"11131","article-title":"Fine-grained dynamic head for object detection","volume":"33","author":"Song","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., and Tian, Q. (2018, January 8\u201314). The unmanned aerial vehicle benchmark: Object detection and tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.neucom.2019.07.073","article-title":"Scale adaptive image cropping for UAV object detection","volume":"366","author":"Zhou","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1109\/JSTARS.2020.3043109","article-title":"DRL-GAN: Dual-stream representation learning GAN for low-resolution image classification in UAV applications","volume":"14","author":"Xi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","unstructured":"Yang, F., Fan, H., Chu, P., Blasch, E., and Ling, H. (November, January 27). Clustered object detection in aerial images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., and Yan, S. (2017, January 21\u201326). Perceptual generative adversarial networks for small object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.211"},{"key":"ref_29","unstructured":"Bell, S., Zitnick, C.L., Bala, K., and Girshick, R. (July, January 26). Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Paradise, NV, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3039","DOI":"10.1109\/TMM.2020.2971175","article-title":"Hierarchical context features embedding for object detection","volume":"22","author":"Qiu","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_31","unstructured":"Li, Y., Chen, Y., Wang, N., and Zhang, Z. (November, January 27). Scale-aware trident networks for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1109\/TIP.2017.2773199","article-title":"Random access memories: A new paradigm for target detection in high resolution aerial remote sensing images","volume":"27","author":"Zou","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bai, Y., Zhang, Y., Ding, M., and Ghanem, B. (2018, January 8\u201314). Sod-mtgan: Small object detection via multi-task generative adversarial network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_13"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, P., and Ramanan, D. (2017, January 21\u201326). Finding tiny faces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.166"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mukhiddinov, M., and Cho, J. (2021). Smart glass system using deep learning for the blind and visually impaired. Electronics, 10.","DOI":"10.3390\/electronics10222756"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.1109\/TIP.2019.2896952","article-title":"VSSA-NET: Vertical spatial sequence attention network for traffic sign detection","volume":"28","author":"Yuan","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4716","DOI":"10.1109\/TSMC.2019.2945053","article-title":"Modular lightweight network for road object detection using a feature fusion approach","volume":"51","author":"Liu","year":"2019","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xiang, W., Zhang, D.Q., Yu, H., and Athitsos, V. (2018, January 12\u201315). Context-aware single-shot detector. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00198"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Wang, K., Zhu, X., and Wang, X. (2017, January 22\u201329). Chained cascade network for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.214"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Singh, B., and Davis, L.S. (2018, January 18\u201322). An analysis of scale invariance in object detection snip. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00377"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lyu, P., Yao, C., Wu, W., Yan, S., and Bai, X. (2018, January 18\u201322). Multi-oriented scene text detection via corner localization and region segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00788"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 16\u201320). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_44","unstructured":"Zoph, B., and Le, Q.V. (2017). Neural architecture search with reinforcement learning. Int. Conf. Learn. Represent., 1\u201316."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 14\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., and Le, Q.V. (2019, January 16\u201320). Nas-fpn: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1023\/A:1016328200723","article-title":"Vision and the atmosphere","volume":"48","author":"Narasimhan","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., and Koltun, V. (2021, January 19\u201325). Vision transformers for dense prediction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"108548","DOI":"10.1016\/j.patcog.2022.108548","article-title":"mSODANet: A Network for Multi-Scale Object Detection in Aerial Images using Hierarchical Dilated Convolutions","volume":"126","author":"Chalavadi","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yu, W., Yang, T., and Chen, C. (2021, January 3\u20138). Towards resolving the challenge of long-tail distribution in UAV images for object detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00330"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, Y., and Zhao, X. (2020, January 23\u201328). Object detection using clustering algorithm adaptive searching regions in aerial images. Proceedings of the ECCV, Glasgow, UK.","DOI":"10.1007\/978-3-030-66823-5_39"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, Z., Gao, G., Sun, L., and Fang, Z. (2021, January 5\u20139). HRDNet: High-resolution detection network for small objects. Proceedings of the ICME, Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428241"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 11\u201317). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios. Proceedings of the ICCVW, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_57","unstructured":"Jocher, G. (2022, August 01). YOLOv5. Available online: https:\/\/github.com\/ultralytics\/yolov5."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3919\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:08:01Z","timestamp":1760141281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,12]]},"references-count":57,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14163919"],"URL":"https:\/\/doi.org\/10.3390\/rs14163919","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,12]]}}}