Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3
Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting an...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/2889115 |
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| _version_ | 1832554606298660864 |
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| author | Chun Liu Jian Li |
| author_facet | Chun Liu Jian Li |
| author_sort | Chun Liu |
| collection | DOAJ |
| description | Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. 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’ 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-correction 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-correction 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. |
| format | Article |
| id | doaj-art-f89170ac5bb641128c4b8cea6df3ce6d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-f89170ac5bb641128c4b8cea6df3ce6d2025-02-03T05:51:12ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/28891152889115Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3Chun Liu0Jian Li1Hubei University of Technology School of Computer Science, Wuhan 430068, ChinaHubei University of Technology School of Computer Science, Wuhan 430068, ChinaAutomatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. 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’ 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-correction 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-correction 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.http://dx.doi.org/10.1155/2021/2889115 |
| spellingShingle | Chun Liu Jian Li Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3 Complexity |
| title | Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3 |
| title_full | Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3 |
| title_fullStr | Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3 |
| title_full_unstemmed | Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3 |
| title_short | Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3 |
| title_sort | self correction ship tracking and counting with variable time window based on yolov3 |
| url | http://dx.doi.org/10.1155/2021/2889115 |
| work_keys_str_mv | AT chunliu selfcorrectionshiptrackingandcountingwithvariabletimewindowbasedonyolov3 AT jianli selfcorrectionshiptrackingandcountingwithvariabletimewindowbasedonyolov3 |