{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:20:37Z","timestamp":1750220437549,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":7,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T00:00:00Z","timestamp":1602892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,17]]},"DOI":"10.1145\/3438872.3439109","type":"proceedings-article","created":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T17:12:34Z","timestamp":1608916354000},"page":"370-374","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Ship target detection and fine-class recognition based on course-to-fine cascade neural networks"],"prefix":"10.1145","author":[{"given":"Li","family":"Shuxin","sequence":"first","affiliation":[{"name":"School of Information and Communication, National University of Defense Technology, Xi'an, China"}]},{"given":"Zhou","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Information and Communication, National University of Defense Technology, Xi'an, China"}]}],"member":"320","published-online":{"date-parts":[[2020,12,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.03.033"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18082702"},{"key":"e_1_3_2_1_3_1","series-title":"Lecture Notes in Computer Science","volume-title":"Part-based R-CNNs for finegrained category detection[C]","author":"Zhang Ning","year":"2014","unstructured":"Ning Zhang , Jeff Donahue , Ross Girshick , Part-based R-CNNs for finegrained category detection[C] . Lecture Notes in Computer Science . Springer Verlag , 2014 : 834-849. Ning Zhang, Jeff Donahue, Ross Girshick, et al. Part-based R-CNNs for finegrained category detection[C]. Lecture Notes in Computer Science. Springer Verlag, 2014:834-849."},{"volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society","author":"Angelova Anelia","key":"e_1_3_2_1_4_1","unstructured":"Anelia Angelova , Shenghuo Zhu . Efficient object detection and segmentation for fine-grained recognition[C] . Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society , 2013: 811-818. Anelia Angelova, Shenghuo Zhu. Efficient object detection and segmentation for fine-grained recognition[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2013:811-818."},{"volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society","author":"Yang Linjie","key":"e_1_3_2_1_5_1","unstructured":"Linjie Yang , Ping Luo , Chen Change Loy , A large-scale car dataset for fine-grained categorization and verification[C] . Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society , 2015: 3973-3981. Linjie Yang, Ping Luo, Chen Change Loy, et al. A large-scale car dataset for fine-grained categorization and verification[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2015:3973-3981."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.002"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5220\/0006120603240331"}],"event":{"name":"RICAI 2020: 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence","acronym":"RICAI 2020","location":"Shanghai China"},"container-title":["Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3438872.3439109","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3438872.3439109","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:19Z","timestamp":1750193239000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3438872.3439109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,17]]},"references-count":7,"alternative-id":["10.1145\/3438872.3439109","10.1145\/3438872"],"URL":"https:\/\/doi.org\/10.1145\/3438872.3439109","relation":{},"subject":[],"published":{"date-parts":[[2020,10,17]]},"assertion":[{"value":"2020-12-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}