Ship track classification method based on LSTM-ResNet model

Qiang Wu; Jingfeng Zang

doi:10.1117/12.2668035

20 April 2023 Ship track classification method based on LSTM-ResNet model

Qiang Wu, Jingfeng Zang

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 1260211 (2023) https://doi.org/10.1117/12.2668035
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Due to the limited field of view of monitoring equipment and the high cost of radar equipment, the traditional method of image identification of ships is not effective. In recent years, with the rise of embedded devices, ship trajectory data has been widely collected, which provides the possibility of ship tracking method. If a simple convolutional neural network is used to extract the data information in the ship track, the timing information in the data cannot be obtained. Therefore, this paper proposes a ship path classification method based on deep learning(LSTM-ResNet model). This model combined with the feature of convolution network extract the path in the boat, LSTM model is utilized to extract implicit in the temporal feature vector data, the characteristics of the two models after the output through the model merge to form a new model, after the former sequence pretreatment process, the data is to heavy, cuts, after filling, ships track recognition model based on convolutional neural network, it has obvious effectiveness.

Citation Download Citation

Qiang Wu and Jingfeng Zang "Ship track classification method based on LSTM-ResNet model", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 1260211 (20 April 2023); https://doi.org/10.1117/12.2668035

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