作者：陈欣佳¹, 刘艳霞¹, 洪晓斌², 王慧芳²

作者单位：1. 华南理工大学软件学院, 广东 广州 510006;
2. 华南理工大学机械与汽车工程学院, 广东 广州 510640

关键词：无人艇;目标检测;目标跟踪;SSD;MobileNets;相关滤波

摘要：

针对目前目标检测和目标跟踪算法对无人艇运算配置要求高、速度慢等问题，该文一种提出基于SSD-CF的无人艇目标检测跟踪方法。利用MobileNets结构结合SSD目标检测算法构建轻量级卷积神经网络，实现无人艇的水面目标检测。目标检测结果作为相关滤波CF目标跟踪算法的初始输入，并在目标跟踪过程的保障其有效性。通过MODD水面船只视频数据实验表明，SSD-CF方法融合目标检测与目标跟踪算法，可有效地降低对运算力的要求，提升目标检测跟踪速度和目标位置的稳定连续性。

Unmanned boat target detection and tracking method based on SSD-CF
CHEN Xinjia¹, LIU Yanxia¹, HONG Xiaobin², WANG Huifang²
1. School of Software Engineering, South China University of Technology, Guangzhou 510006, China;
2. School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Abstract: Aiming at the problems of high performance and slow speed of unmanned boat operation configuration, the unmanned boat target detection and tracking method based on SSD-CF is proposed. The lightweight convolutional neural network is constructed by using MobileNets structure combined with SSD target detection algorithm to realize the surface target detection of unmanned boats. The target detection result is used as the initial input of the CF target tracking algorithm, and effectiveness of target tracking is guaranteed. The video data of MODD surface vessel shows that the SSD-CF method combines the target detection and target tracking algorithms to effectively reduce the computational force requirements and improve the target detection tracking speed and the stable continuity of the target position.
Keywords: unmanned boat;target detection;target tracking;SSD;MobileNets;CF
2019, 45(2):145-150  收稿日期: 2018-12-15;收到修改稿日期: 2019-01-03
基金项目: 广东省科技计划项目（2017B010118002）；广州市科技计划项目（201802020031，201802020021）
作者简介: 陈欣佳(1993-),女,广东汕头市人,硕士研究生,专业方向为人工智能
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