{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T04:22:08Z","timestamp":1728620528805},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031205026"},{"type":"electronic","value":"9783031205033"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20503-3_6","type":"book-chapter","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T12:09:06Z","timestamp":1671192546000},"page":"71-80","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LS-YOLO: Lightweight SAR Ship Targets Detection Based on\u00a0Improved YOLOv5"],"prefix":"10.1007","author":[{"given":"Yaqi","family":"He","sequence":"first","affiliation":[]},{"given":"Zi-Xin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yu-Long","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,17]]},"reference":[{"key":"6_CR1","first-page":"1","volume":"60","author":"D Li","year":"2021","unstructured":"Li, D., Liang, Q., Liu, H., Liu, Q., Liu, H., Liao, G.: A novel multidimensional domain deep learning network for SAR ship detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201313 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"6_CR2","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TGRS.2020.3004911","volume":"59","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Hou, B., Jiao, L.: Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification. IEEE Trans. Geosci. Remote Sens. 59(2), 1200\u20131213 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"6_CR3","first-page":"34","volume":"9","author":"DU Lan","year":"2020","unstructured":"Lan, D.U., Wang, Z.C., Wang, Y., Wei, D., Lu, L.I.: Survey of research progress on target detection and discrimination of single-channel SAR images for complex scenes. Radars 9(1), 34\u201354 (2020)","journal-title":"Radars"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Saepuloh, A., Bakker, E., Suminar, W.: The significance of SAR remote sensing in volcano-geology for hazard and resource potential mapping. In: Proceedings of the AIP Conference Proceedings, article no. 070005 (2017)","DOI":"10.1063\/1.4987093"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Schumacher, R., Schiller, J.: Non-cooperative target identification of battlefield targets-classification results based on SAR images. In: Proceedings of the IEEE International Radar Conference, pp. 167\u2013172 (2005)","DOI":"10.1109\/RADAR.2005.1435813"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhang, X., Zhang, T.: Multi-scale SAR ship classification with convolutional neural network. In: Proceedings of the International Geoscience and Remote Sensing Symposium, pp. 4284\u20134287 (2021)","DOI":"10.1109\/IGARSS47720.2021.9553116"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Yang, Y., Liao, Y., Ni, S., Lin, C.: Study of algorithm for aerial target detection based on lightweight neural network. In: Proceedings of the International Conference on Consumer Electronics and Computer Engineering, pp. 422\u2013426 (2021)","DOI":"10.1109\/ICCECE51280.2021.9342470"},{"issue":"4","key":"6_CR8","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/LGRS.2017.2654450","volume":"14","author":"C Wang","year":"2017","unstructured":"Wang, C., Bi, F., Zhang, W., Chen, L.: An intensity-space domain CFAR method for ship detection in HR SAR images. IEEE Geosci. Remote Sens. Lett. 14(4), 529\u2013533 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"9","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/LGRS.2018.2838263","volume":"15","author":"O Pappas","year":"2018","unstructured":"Pappas, O., Achim, A., Bull, D.: Superpixel-level CFAR detectors for ship detection in SAR imagery. IEEE Geosci. Remote Sens. Lett. 15(9), 1397\u20131401 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"8","key":"6_CR10","first-page":"4511","volume":"52","author":"Z Shi","year":"2013","unstructured":"Shi, Z., Yu, X., Jiang, Z., Li, B.: Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature. IEEE Trans. Geosci. Remote Sens. 52(8), 4511\u20134523 (2013)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"6_CR13","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv: 1804.02767 (2018)"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision \u2013 ECCV 2016. ECCV 2016. Lecture Notes in Computer Science(), vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"104848","DOI":"10.1109\/ACCESS.2019.2930939","volume":"7","author":"C Chen","year":"2019","unstructured":"Chen, C., He, C., Hu, C., Pei, H., Jiao, L.: A deep neural network based on an attention mechanism for SAR ship detection in multiscale and complex scenarios. IEEE Access 7, 104848\u2013104863 (2019)","journal-title":"IEEE Access"},{"issue":"11","key":"6_CR18","doi-asserted-by":"publisher","first-page":"8983","DOI":"10.1109\/TGRS.2019.2923988","volume":"57","author":"Z Cui","year":"2019","unstructured":"Cui, Z., Li, Q., Cao, Z., Liu, N.: Dense attention pyramid networks for multi-scale ship detection in SAR images. IEEE Trans. Geosci. Remote Sens. 57(11), 8983\u20138997 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, R., Yao, J., Zhang, K., Feng, C., Zhang, J.: S-CNN-based ship detection from high-resolution remote sensing images. In: International Archives of the Photogrammetry, Remote Sensing Spatial Information Sciences 41 (2016)","DOI":"10.5194\/isprs-archives-XLI-B7-423-2016"},{"key":"6_CR20","unstructured":"Fu, C. Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C.: DSSD: deconvolutional single shot detector. arXiv preprint arXiv:1701.06659 (2017)"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J. H., Zou, Y., Zhou, D., Feng, J.: Panet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9197\u20139206 (2019)","DOI":"10.1109\/ICCV.2019.00929"},{"issue":"5","key":"6_CR22","doi-asserted-by":"publisher","first-page":"871","DOI":"10.3390\/rs13050871","volume":"13","author":"G Tang","year":"2021","unstructured":"Tang, G., Zhuge, Y., Claramunt, C., Men, S.: N-Yolo: a SAR ship detection using noise-classifying and complete-target extraction. Remote Sens. 13(5), 871 (2021)","journal-title":"Remote Sens."},{"issue":"3","key":"6_CR23","doi-asserted-by":"publisher","first-page":"755","DOI":"10.3390\/rs14030755","volume":"14","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Zhang, M., Wang, H., Tan, J.: Ship detection in SAR images based on multi-scale feature extraction and adaptive feature fusion. Remote Sens. 14(3), 755 (2022)","journal-title":"Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20503-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T15:37:16Z","timestamp":1728574636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20503-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031205026","9783031205033"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20503-3_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"472","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}