Fast pulse detection algorithm based on improved ghost convolution

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.