| 程 亮,杨 渊,张云飞,林德群,杨春利,杨士远,王磊刚,何赟泽.面向无人艇智能感知的水上目标识别算法研究[J].电子测量与仪器学报,2021,35(9):99-104 |
| 面向无人艇智能感知的水上目标识别算法研究 |
| Research on water target recognition algorithm for unmanned surface vessel |
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| DOI： |
| 中文关键词: 无人艇 目标检测 YOLO |
| 英文关键词:USV object detection YOLO |
| 基金项目:云洲研发项目（YZ LX0A1 820）资助 |
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| Author | Institution |
| Cheng Liang | 1. School of Ocean Engineering, Jiangsu Ocean University,2. Zhuhai Yunzhou Intelligent Technology Co. , Ltd |
| Yang Yuan | 3. College of Electrical and Information Engineering,
Hunan University |
| Zhang Yunfei | 1. School of Ocean Engineering, Jiangsu Ocean University,2. Zhuhai Yunzhou Intelligent Technology Co. , Ltd |
| Lin Dequn | 4. Unit 63983 of PLA |
| Yang Chunli | 2. Zhuhai Yunzhou Intelligent Technology Co. , Ltd |
| Yang Shiyuan | 2. Zhuhai Yunzhou Intelligent Technology Co. , Ltd |
| Wang Leigang | 2. Zhuhai Yunzhou Intelligent Technology Co. , Ltd |
| He Yunze | 3. College of Electrical and Information Engineering,
Hunan University |
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| 中文摘要: |
| 针对水面无人艇(unmanned surface vessel, USV)智能感知系统对图像处理过程的准确性和实时性要求,研究了一种根
据无人艇上机载视觉传感器对水上目标进行识别与定位的算法。 首先根据开源数据集与实验数据采集图像,对实验数据抽帧、
去重、标注、统计,创建了水上目标识别数据库 YZ10K;其次实践了主流的基于深度学习的目标检测方法,包括 Faster R-CNN、
SSD、YOLOv3 等;最后针对水上目标特点,提出了一种基于改进 YOLOv3 的增强型轻量级水上目标检测网络 WT-YOLO(water
target-you only look once)。 无人船实验验证表明,WT-YOLO 算法取得了准确且快速的目标识别效果,平均精度为 79. 30%,处理
速度为 30. 01 fps。 |
| 英文摘要: |
| In this paper, a water-target recognition algorithm based on the data acquired by the onboard visual sensor from unmanned
surface vessels (USV) is reported, in order to satisfy the accuracy and speed requirements of USV intelligent sensing system. The main
outcome are summarized as follows: First, images are collected based on open source datasets and experimental data, to create a watertarget recognition database which named YZ10K; second, popular deep-learning based target detection methods including Faster RCNN, SSD, YOLOv3, etc. are implemented and compared; third, based on the characteristics of water targets, an enhanced lightweight
Water Target detection network WT-YOLO (water target-YOLO) is proposed. The experimental verification shows that the WT-YOLO
algorithm based on improved YOLOv3 has achieved accurate and real-time target recognition with the mean average precision (mAP) of
79. 30% and frame per second of 30. 01. |
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