Target recognition method for Guangdong-Hong Kong-Macao Greater Bay Area port based on deep residual neural network

Ming Xin

doi:10.1117/12.3004558

11 October 2023 Target recognition method for Guangdong-Hong Kong-Macao Greater Bay Area port based on deep residual neural network

Ming Xin

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128003U (2023) https://doi.org/10.1117/12.3004558
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

The traditional port target recognition algorithm has the problems of high time complexity and low recognition accuracy. In order to solve this problem, we propose a method of Guangdong-Hong Kong-Macao Greater Bay Area port target recognition based on deep residual neural network. By studying various network models with different deep and parameters, a new multi-strapdown residual network model (Mu-ResNet) with less memory occupation and lower time complexity is designed by combining two-layer residual learning module with three-layer residual learning module. The experimental results show that the recognition rate of our proposed method is better than the traditional recognition algorithm on the port target dataset.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming Xin "Target recognition method for Guangdong-Hong Kong-Macao Greater Bay Area port based on deep residual neural network", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128003U (11 October 2023); https://doi.org/10.1117/12.3004558

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