关键词: 目标检测, 钻井平台, 卷积神经网络, 模型训练, SAR影像

Abstract: Convolutional neural networks (CNN) is the most representative network structure of deep learning (DL). Synthetic aperture radar (SAR) image itself has the position structure relationship, the characteristics of the CNN model determine that it can use the position structure relationship of the image to extract the features of the image better,so it is more suitable to use the CNN model for marine target detection.The paper based on CNN framework constructs the DL model Ocean TDAx of offshore drilling platform detection, trains and tests the OceanTDAx model through the improved WinR-Adagrad gradient training algorithm, experimental results show that the Oceant TDA9 model is the highest accuracy. For the Ocean TDA9 model, seven model training algorithms, such as adam, RMSprop, Stochastic gradient descent (SGD), Adagrad and Momentum, is used to conduct experiments,and the training loss and accuracy with relevance of training batches of different algorithmsis is compared. Based on the polarized SAR data of the Bohai sea, the proposed Ocean TDA9 model and the existing CNN model and Visual geometry group (VGG) model are used to compare the detection experiments of offshore drilling platforms.The results show that the constructed Ocean TDA9 model is excellent overall performance in drilling platform testing.

Key words: target detection, drilling platform, convolutional neural network, model training, SAR image