对船舶图像进行快速准确识别在军民领域都有广泛应用，随着船舶种类的增多、图像质量的提高，传统的卷积神经网络进行船舶图像识别需耗费大量时间。本文对深度神经网络的原理进行分析，并在此基础上研究基于深度神经网络的船舶图像识别流程，对船舶图像预处理技术进行研究，建立船舶图像训练集和测试集，对YOLOV2、卷积神经网络和本文算法的平均识别时间和识别准确率进行分析测试，最后研究3种算法的训练次数对识别准确率的影响。本文研究的深度神经网络船舶图像识别算法，在平均识别时间以及识别准确率上具有一定优势。
Fast and accurate recognition of ship images is widely used in both civil and military fields. With the increase of ship types and the improvement of image quality, it takes a lot of time to adapt the traditional convolutional neural network to ship image recognition. In this paper, the principle of deep neural network is analyzed, and on this basis, the ship image recognition process based on deep neural network is studied, the ship image preprocessing technology is studied, the ship image training set and test set are established, and the average recognition time and recognition accuracy of YOLOV2, convolutional neural network and the algorithm in this paper are analyzed. Finally, the influence of the training times of the three algorithms on the recognition accuracy is studied. The deep neural network ship image recognition algorithm studied in this paper has certain advantages in average recognition time and recognition accuracy.
2024,46(3): 174-177 收稿日期：2023-09-27
DOI：10.3404/j.issn.1672-7649.2024.03.032
分类号：TP391
基金项目：广东省“攀登计划”项目（pdjh2023b0787）；广东省教育厅实验教学示范中心类项目（粤教高函(2023)4号）；湛江科技学院品牌提升计划项目（PPJH2021008）
作者简介：赵圆圆（1980 – ），女，硕士，副教授，研究方向为计算机应用
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