{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:42:07Z","timestamp":1769150527826,"version":"3.49.0"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T00:00:00Z","timestamp":1631577600000},"content-version":"vor","delay-in-days":256,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB1600605"],"award-info":[{"award-number":["2019YFB1600605"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real\u2010time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD\u2010SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD\u2010SSD uses visual images captured by surveillance cameras to achieve real\u2010time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects\u2019 performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the <jats:italic>K<\/jats:italic>\u2010means clustering algorithm, the Intersection\u2010over\u2010Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R\u2010CNN, SSD, and YOLOv3). Moreover, our model\u2019s FPS is 45, which can meet real\u2010time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.<\/jats:p>","DOI":"10.1155\/2021\/7018035","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T20:35:07Z","timestamp":1631651707000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["NSD\u2010SSD: A Novel Real\u2010Time Ship Detector Based on Convolutional Neural Network in Surveillance Video"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1959-8597","authenticated-orcid":false,"given":"Jiuwu","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1182-4537","authenticated-orcid":false,"given":"Zhijing","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0336-3977","authenticated-orcid":false,"given":"Shanshan","family":"Liang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"DugadS. 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