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.
The convolutional neural network (CNN) in deep learning artificial intelligence (AI) has developed rapidly in recent years, delivering many achievements to other areas of economic life. Nevertheless, gaps in CNN-related research still exist in the field of object identification and detection in regard to active sonar images, as most research in this field is still dominated by classical algorithms. Therefore, this paper summarizes the YOLOV5 used, analyzes the existing network defects, and optimizes the identification and detection algorithms based on the YOLOV5 network framework. The practical detection sets a high requirement for the precision of the sonar pulse signals detected. Specifically, it requires the false alarm rate to be lower than the designed value and the errors in the detection parameters to be kept within the tolerable range. To increase the detection precision, this paper adds an attention enhancement module to the network based on the original YOLOV5, which significantly improves the detection parameter effects.
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