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YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV color histogram features and LBP local features\u2019 target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. Since tracking targets are subject to drift and jitter, a self\u2010correction network that composites both direction judgment based on regression and target counting method with variable time windows is designed, which better realizes automatic detection, tracking, and self\u2010correction of moving object numbers in water. The method in this paper shows stability and robustness, applicable to the automatic analysis of waterway videos and statistics extraction.<\/jats:p>","DOI":"10.1155\/2021\/2889115","type":"journal-article","created":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T12:57:56Z","timestamp":1622552276000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["[Retracted] Self\u2010Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5275-7991","authenticated-orcid":false,"given":"Chun","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"40","article-title":"Advances on visual object tracking in past decade","volume":"48","author":"Zhang K. 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