Dynamic display algorithm of sonar data based on grayscale distribution model and computational intelligence

Existing image processing and target recognition algorithms have limitations in complex underwater environments and dynamic changes, making it difficult to ensure real-time and precision. Multiple noise sources interfere with sonar signals, which affects both data precision and clarity. This article studies the dynamic display algorithm of sonar data based on grayscale distribution model and computational intelligence. It proposes to construct a grayscale distribution model for sonar images, analyze the grayscale histogram, determine the threshold selection of the maximum entropy threshold segmentation method, and finally complete the target segmentation. The segmented images can be used to train the convolutional neural network object recognition model constructed in this article. To verify the effectiveness of the proposed method, a test set was used to evaluate the trained target recognition model. The precision of the model recognition was 87.95%, the recall was 87.97%, and the F1 value was 0.8794, which is significantly higher than the traditional model (Such as Otsu and SVM is below 80%). The recognition speed reached 37 m, which is a certain improvement compared to the traditional model.