A study on the pulse parameter detection based on the improved YOLOV5

Abstract

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.