| 基于TBD策略的船舶交通流视觉图像统计方法 |
| 投稿时间：2020-08-23 修订日期：2021-02-27 点此下载全文 |
| 引用本文：关克平,韩笑,蒋宇.基于TBD策略的船舶交通流视觉图像统计方法[J].上海海事大学学报,2021,42(2):40-44. |
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| 基金项目:国家自然科学基金(51909155) |
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| 中文摘要:为提高在天气恶劣、目标密集、目标被遮挡及其他复杂海况下船舶交通流统计的准确率，提出一种将目标检测算法CenterNet、多目标跟踪算法DeepSORT与凸包算法中优化逆时针(counter clockwise，CCW)判断的单线法相结合的船舶交通流视觉图像统计方法。使用Python对所选的数据和场景进行测试，结果表明：CenterNet在多场景检测中比YOLOv3更优秀；基于目标检测的多目标跟踪算法具有良好实时性，能够有效对抗因目标抖动、密集、被遮挡等所导致的目标丢失，继而减少船舶交通流统计时常出现的漏检、错检和重复统计等问题。 |
| 中文关键词:CenterNet DeepSORT 凸包检测 船舶交通流统计 目标检测 |
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| A visual image statistics method for ship traffic flow based on TBD strategy |
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| Abstract:In order to improve the accuracy of ship traffic flow statistics under severe weather, dense targets, occluded targets and other complex sea conditions, a visual image statistics method for ship traffic flow is proposed, where the target detection algorithm CenterNet and the multi target tracking algorithm DeepSORT are combined with the single line method for the counter clockwise (CCW) judgment optimization in the convex hull algorithm. Python is adopted to test the selected data and scenes. The results show that: CenterNet is better than YOLOv3 in the multi scene detection; the multi target tracking algorithm based on target detection is of good real time performance, and can effectively combat target loss caused by target jitter, density and occlusion, etc., and then reduce the problems such as missed detection, false detection, and repeated statistics in traffic flow statistics. |
| keywords:CenterNet DeepSORT convex hull detection ship traffic flow statistics target detection |
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