基于无人船视觉的水域人员类别识别算法

Person category identification algorithm in water environment based on unmanned ship vision

针对水域环境下人员识别,提出了一种基于水面无人船(unmanned surface ship, USV)视觉传感器的水域人员类别识别算法。依照数据采集与模型更新流程,将采集到的视频数据进行数据清洗与标记后,创建人员类别数据集39 959张图片,7个类别;实践了基于深度学习方法下主流目标检测网络YOLO v5,并针对水域环境场景特点,提出基于YOLO v5的人员类别识别算法;将人员类别识别算法部署到边缘计算平台,实现算法在无人船上的实时应用。算法在人员类别识别数据集上达到了平均精度86%,在无人船实测中实现了每秒处理38帧的人员类别识别实时性表现。

To achieve person recognition in water environment, a person category identification algorithm based on vision sensors on unmanned surface ship(USV) is proposed. Firstly, base on the data acquisition and model update workflow, a person category dataset of 39 959 pictures and 7 categories is created after data cleaning and labeling on original videos. Secondly, YOLO v5, the mainstream object detection network in the field of deep learning method, is practiced, and an improved person category identification algorithm based on YOLO v5 is proposed according to the characteristics of water environment scenes. Thirdly, the algorithm is deployed to the edge computing platform to realize the real-time use of the algorithm on the unmanned ship. The algorithm achieves an average accuracy of 86% on our dataset and achieves real-time performance of processing 38 frames per second with accurate person recognition in the unmanned ship test.