{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:18:25Z","timestamp":1774444705914,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T00:00:00Z","timestamp":1597276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771027, 61071139, 61471019, 61501011, 61171122"],"award-info":[{"award-number":["61771027, 61071139, 61471019, 61501011, 61171122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/M026981\/1"],"award-info":[{"award-number":["EP\/M026981\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/N011074\/1"],"award-info":[{"award-number":["EP\/N011074\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Royal Society-Newton Advanced Fellowship","award":["NA160342"],"award-info":[{"award-number":["NA160342"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["Marie Sklodowska Curie Grant Agreement No. 720325."],"award-info":[{"award-number":["Marie Sklodowska Curie Grant Agreement No. 720325."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learning-based algorithms bring promising performance due to end-to-end detection and automated feature extraction. However, several challenges still exist: (1) standard deep learning detectors based on anchors have certain unsolved problems, such as tuning of anchor-related parameters, scale-variation and high computational costs. (2) SAR data is huge but the labeled data is relatively small, which may lead to overfitting in training. (3) To improve detection speed, deep learning detectors generally detect targets based on low-resolution features, which may cause missed detections for small targets. In order to address the above problems, an anchor-free convolutional network with dense attention feature aggregation is proposed in this paper. Firstly, we use a lightweight feature extractor to extract multiscale ship features. The inverted residual blocks with depth-wise separable convolution reduce the network parameters and improve the detection speed. Secondly, a novel feature aggregation scheme called dense attention feature aggregation (DAFA) is proposed to obtain a high-resolution feature map with multiscale information. By combining the multiscale features through dense connections and iterative fusions, DAFA improves the generalization performance of the network. In addition, an attention block, namely spatial and channel squeeze and excitation (SCSE) block is embedded in the upsampling process of DAFA to enhance the salient features of the target and suppress the background clutters. Third, an anchor-free detector, which is a center-point-based ship predictor (CSP), is adopted in this paper. CSP regresses the ship centers and ship sizes simultaneously on the high-resolution feature map to implement anchor-free and nonmaximum suppression (NMS)-free ship detection. The experiments on the AirSARShip-1.0 dataset demonstrate the effectiveness of our method. The results show that the proposed method outperforms several mainstream detection algorithms in both accuracy and speed.<\/jats:p>","DOI":"10.3390\/rs12162619","type":"journal-article","created":{"date-parts":[[2020,8,14]],"date-time":"2020-08-14T08:28:35Z","timestamp":1597393715000},"page":"2619","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1489-0812","authenticated-orcid":false,"given":"Fei","family":"Gao","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1771-6027","authenticated-orcid":false,"given":"Yishan","family":"He","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-082X","authenticated-orcid":false,"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[{"name":"Cognitive Big Data and Cyber-Informatics (CogBID) Laboratory, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Leicester, Leicester LE1 7RH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,13]]},"reference":[{"key":"ref_1","unstructured":"Crisp, D. (2004). The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery, Australian Government, Department of Defense."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1109\/JSTARS.2017.2787573","article-title":"Detection and discrimination of ship targets in complex background from spaceborne alos-2 sar images","volume":"11","author":"Ao","year":"2018","journal-title":"IEEE J. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huo, W., Huang, Y., Pei, J., Zhang, Q., Gu, Q., and Yang, J. (2018). Ship detection from ocean sar image based on local contrast variance weighted information entropy. Sensors, 18.","DOI":"10.3390\/s18041196"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/LGRS.2016.2631638","article-title":"Synthetic aperture radar ship detection using haar-like features","volume":"14","author":"Schwegmann","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/LGRS.2010.2048697","article-title":"A new cfar ship detection algorithm based on 2-d joint log-normal distribution in sar images","volume":"7","author":"Ai","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, N., Cao, Z., Cui, Z., Pi, Y., and Dang, S. (2019). Multi-scale proposal generation for ship detection in sar images. Remote Sens., 11.","DOI":"10.3390\/rs11050526"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5733","DOI":"10.1080\/01431160802089887","article-title":"A scheme for ship detection in inhomogeneous regions based on segmentation of sar images","volume":"29","author":"Zhang","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/LGRS.2005.845033","article-title":"A novel algorithm for ship detection in sar imagery based on the wavelet transform","volume":"2","author":"Tello","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","first-page":"20","article-title":"Ship detection in sar imagery based on the wavelet transform","volume":"584","author":"Tello","year":"2005","journal-title":"ESASP"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Leng, X., Ji, K., Zhou, S., Xing, X., and Zou, H. (2016). An adaptive ship detection scheme for spaceborne sar imagery. Sensors, 16.","DOI":"10.3390\/s16091345"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/LGRS.2013.2248118","article-title":"Ship detection for high-resolution sar images based on feature analysis","volume":"11","author":"Wang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Letters"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7763","DOI":"10.1080\/01431161.2014.976887","article-title":"A polsar ship detector based on a multi-polarimetric-feature combination using visual attention","volume":"35","author":"Wang","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.knosys.2011.07.016","article-title":"Ann vs. Svm: Which one performs better in classification of mccs in mammogram imaging","volume":"26","author":"Ren","year":"2012","journal-title":"Knowl.-Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2019). High-speed ship detection in sar images based on a grid convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11101206"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kang, M., Leng, X., Lin, Z., and Ji, K. (2017, January 18\u201321). A Modified Faster R-CNN Based on CFAR Algorithm for SAR Ship Detection. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958815"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1016\/j.cja.2019.03.021","article-title":"A novel visual attention method for target detection from sar images","volume":"32","author":"Fei","year":"2019","journal-title":"Chin. J. Aeronaut."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Tran. Neural Netw. Learn. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yue, Z., Gao, F., Xiong, Q., Wang, J., Huang, T., Yang, E., and Zhou, H. (2019). A novel semi-supervised convolutional neural network method for synthetic aperture radar image recognition. Cogn. Comput., 1\u201312.","DOI":"10.1007\/s12559-019-09639-x"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s12559-018-9563-z","article-title":"A new algorithm of sar image target recognition based on improved deep convolutional neural network","volume":"11","author":"Gao","year":"2019","journal-title":"Cogn. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s12559-018-9606-5","article-title":"A novel ship target detection algorithm based on error self-adjustment extreme learning machine and cascade classifier","volume":"11","author":"Zhang","year":"2019","journal-title":"Cogn Comput."},{"key":"ref_21","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, Morgan Kaufmann."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 8\u201316). 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_23","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(CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, M.-h., Xu, P., and Guo, Z.-w. (2017, January 18\u201321). Sar ship detection using sea-land segmentation-based convolutional neural network. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958806"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"50693","DOI":"10.1109\/ACCESS.2018.2869289","article-title":"A cascade coupled convolutional neural network guided visual attention method for ship detection from sar images","volume":"6","author":"Zhao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"42301","DOI":"10.1007\/s11432-017-9405-6","article-title":"A coupled convolutional neural network for small and densely clustered ship detection in sar images","volume":"62","author":"Zhao","year":"2019","journal-title":"Sci. China Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., Leng, X., and Lin, Z. (2017). Contextual region-based convolutional neural network with multilayer fusion for sar ship detection. Remote Sens., 9.","DOI":"10.3390\/rs9080860"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8983","DOI":"10.1109\/TGRS.2019.2923988","article-title":"Dense attention pyramid networks for multi-scale ship detection in sar images","volume":"57","author":"Cui","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gao, F., Shi, W., Wang, J., Yang, E., and Zhou, H. (2019). Enhanced feature extraction for ship detection from multi-resolution and multi-scene synthetic aperture radar (sar) images. Remote Sens., 11.","DOI":"10.3390\/rs11222694"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104848","DOI":"10.1109\/ACCESS.2019.2930939","article-title":"A deep neural network based on an attention mechanism for sar ship detection in multiscale and complex scenarios","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Shi, J., and Wei, S. (2019). Depthwise separable convolution neural network for high-speed sar ship detection. Remote Sens., 11.","DOI":"10.3390\/rs11212483"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chang, Y.-L., Anagaw, A., Chang, L., Wang, Y.C., Hsiao, C.-Y., and Lee, W.-H. (2019). Ship detection based on yolov2 for sar imagery. Remote Sens., 11.","DOI":"10.3390\/rs11070786"},{"key":"ref_33","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(CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, L. (2006, January 20\u201324). Efficient non-maximum suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). Cornernet: Detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_36","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE International Conference on Computer Vision(ICCV), Seoul, Korea."},{"key":"ref_37","unstructured":"Yang, Z., Liu, S., Hu, H., Wang, L., and Lin, S. (November, January 27). Reppoints: Point set representation for object detection. Proceedings of the IEEE International Conference on Computer Vision(ICCV), Seoul, Korea."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fan, Q., Chen, F., Cheng, M., Lou, S., Xiao, R., Zhang, B., Wang, C., and Li, J. (2019). Ship detection using a fully convolutional network with compact polarimetric sar images. Remote Sens., 11.","DOI":"10.3390\/rs11182171"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"69742","DOI":"10.1109\/ACCESS.2020.2985637","article-title":"Efficient low-cost ship detection for sar imagery based on simplified u-net","volume":"8","author":"Mao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_42","first-page":"269","article-title":"System design and key technologies of the gf-3 satellite","volume":"46","author":"Qingjun","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/ACCESS.2017.2777444","article-title":"Visual saliency modeling for river detection in high-resolution sar imagery","volume":"6","author":"Gao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.1109\/TGRS.2018.2889353","article-title":"Learning deep ship detector in sar images from scratch","volume":"57","author":"Deng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., and Deng, J. (2016, January 8\u201316). Stacked hourglass networks for human pose estimation. Proceedings of the European Conference on Computer Vision(ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, D., Shelhamer, E., and Darrell, T. (2018, January 18\u201322). Deep layer aggregation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00255"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Roy, A.G., Navab, N., and Wachinger, C. (2018, January 16\u201320). Concurrent spatial and channel \u2018squeeze & excitation\u2019in fully convolutional networks. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"ref_51","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_53","first-page":"852","article-title":"Air-sarship\u20131.0: High resolution sar ship detection dataset","volume":"8","author":"Xian","year":"2019","journal-title":"J. Radars"},{"key":"ref_54","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_55","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, Morgan Kaufmann."},{"key":"ref_56","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_57","unstructured":"(2020, May 01). Darknet: Open Source Neural Networks in C. Available online: http:\/\/pjreddie.com\/darknet\/."},{"key":"ref_58","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). Mmdetection: Open mmlab detection toolbox and benchmark. arXiv."},{"key":"ref_59","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(CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/16\/2619\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:00:32Z","timestamp":1760176832000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/16\/2619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,13]]},"references-count":59,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12162619"],"URL":"https:\/\/doi.org\/10.3390\/rs12162619","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,13]]}}}