Fast pulse detection algorithm based on improved ghost convolution

Jiaxing Qiu; Weiguo Dai; Zhe Chen; Yaping Yin; Qijun Liu; Yinghao Wang

doi:10.1117/12.2684578

19 October 2023 Fast pulse detection algorithm based on improved ghost convolution

Jiaxing Qiu, Weiguo Dai, Zhe Chen, Yaping Yin, Qijun Liu, Yinghao Wang

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127093W (2023) https://doi.org/10.1117/12.2684578
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Fast pulse detection in pulse images has received widespread attention in the field of underwater computer vision, and deep learning-based fast pulse detection algorithms can clearly represent the characteristics of sonar pulses, thus gradually becoming an important research direction in this field. To address the issues of large network parameters and high computational complexity in mainstream sonar pulse detection and recognition algorithms, a lightweight convolutional network that incorporates ghost convolution is designed and applied to fast pulse detection in sonar. The ghost convolution module is designed and placed in different areas to further reduce the network parameters from the network structure. The experiment is conducted on the constructed pulse dataset, and the results show that compared with the traditional model, the sonar pulse fast detection algorithm based on this lightweight convolutional neural network can achieve higher recognition accuracy while maintaining a lower network parameter amount.

Citation Download Citation

Jiaxing Qiu, Weiguo Dai, Zhe Chen, Yaping Yin, Qijun Liu, and Yinghao Wang "Fast pulse detection algorithm based on improved ghost convolution", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127093W (19 October 2023); https://doi.org/10.1117/12.2684578

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