作者单位:

浙江交通职业技术学院 海运学院，浙江 杭州 311112Zhejiang Institute of Communication, Marine Department, Hangzhou 311112, China

大数据分析;港口停泊;船舶识别;Hadoop;分水岭分割;卷积神经网络big data analysis; to lay in port; ship identification; Hadoop; watershed segmentation; convolutional neural network

针对港口图像背景复杂、数据量大，影响船舶识别效率的问题，提出基于大数据分析的港口停泊船舶识别方法。选取Hadoop云计算框架，利用分布式处理方法，并行处理海量港口数据。Hadoop利用数据分析平台，运行Top-hat分水岭分割方法，该方法利用港口图像的局部极小值确定分水岭位置，计算分水岭位置的梯度函数，依据设置的阈值分割港口图像。将分割后的图像作为卷积神经网络的输入，卷积神经网络通过对图像实施卷积和池化操作，输出港口停泊船舶识别结果。实验结果表明，该方法可以精准识别晴天、雨天等不同背景下的港口停泊船舶，船舶识别时间低于40 ms。In order to solve the problems of complex background and large data volume of port image, which affect the efficiency of ship identification, a method of berth ship identification based on big data analysis is studied. Select Hadoop cloud computing framework and use distributed processing method to process massive port data in parallel. Hadoop uses the data analysis platform to run the Top-hat watershed segmentation method, which uses the local minimum value of the port image to determine the watershed position, calculates the gradient function of the watershed position, and divides the port image according to the threshold value set. The segmented image is used as the input of the convolutional neural network. The convolutional neural network performs convolution and pooling operations on the image to output the results of berth ship recognition in port. Experimental results show that the proposed method can accurately identify ships in port under different backgrounds, such as sunny, rainy and so on, and the recognition time is less than 40 ms.

参考文献：

[1] 赵龙飞, 姜晓轶, 孙苗, 等. 面向海运统计的AIS大数据挖掘分析研究[J]. 海洋科学, 2021, 45(12): 55–64

ZHAO Longfei, JIANG Xiaoyi, SUN Miao, et al. AIS big data mining for maritime statistics[J]. Marine Sciences, 2021, 45(12): 55–64

[2] 付哲泉, 李尚生, 李相平, 等. 基于高效可扩展改进残差结构神经网络的舰船目标识别技术[J]. 涤与信息学报, 2020, 42(12): 3005–3012

[3] 马啸, 邵利民, 卢惠民, 等. 一种基于视觉感知的舰船目标智能化识别方法[J]. 电讯技术, 2020, 60(10): 1133–1141

MA Xiao, SHAO Limin, LU Huimin, et al. An Intelligent Ship Targets Recognition Method Based on Visual Perception[J]. Telecommunication Engineering, 2020, 60(10): 1133–1141

[4] 孙嘉赤,焕新, 邓志鹏, 等. 基于级联卷积神经网络的港口多方向舰船检测与分类[J]. 系统工程与涤技术, 2020, 42(9): 1903–1910

[5] 关欣, 国佳恩, 衣晓. 基于低秩双线性池化注意力网络的舰船目标识别[J]. 系统工程与涤技术, 2023, 45(5): 1305–1314

GUAN Xin, GUO Jiaen, YI Xiao. Ship target recognition based on low rank bilinear pooling attention network[J]. Systems Engineering and Electronics, 2023, 45(5): 1305–1314