近年来，基于无人机的海上探测与侦察技术获得了迅速发展，利用无人机进行海上船舶目标的识别已经成为一项热点研究，基于图像的目标识别也是其中的重点和难点。传统的图像分割包括边缘检测、聚类分割等，本文结合Grabcut算法和卷积神经网络技术，开发一种新型船舶影响目标识别技术，在提高图像目标识别效率的基础上也提高了目标识别的精度，具有实际应用价值。
In recent years, the sea detection and reconnaissance technology based on UAV has achieved rapid development. The recognition of ship targets at sea using UAV has become a hot research work, and the target recognition based on image is also one of the key and difficult points. Traditional image segmentation includes edge detection, clustering segmentation, etc. This paper combines Grabcut algorithm and convolutional neural network technology to develop a new ship impact target recognition technology, which improves the accuracy of target recognition on the basis of improving the efficiency of image target recognition, and has important practical application value.
2022,44(20): 165-168 收稿日期：2022-03-09
DOI：10.3404/j.issn.1672-7649.2022.20.034
分类号：U604.26
作者简介：苏立平(1976-)，女，讲师，研究方向为数字图像处理技术、影视技术与艺术
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