Evaluating the Adaptability of Deep Learning-Based Multi-feature Sonar Image Detection Algorithms

In the underwater environment, characterized by inherent complexities and prevalent noise, analyzing sonar images and detecting objects is challenging. This study employs a deep learning approach utilizing a convolutional neural network (CNN) to enhance sonar image analysis through multi-feature detection and fusion. The Visual Geometry Group Network 16 (VGG-16), a model of CNN, is utilized initially for feature extraction from sonar images. Subsequently, a weighted feature fusion technique is applied to amalgamate the feature vectors extracted by the CNN, thus forming an integrated multi-feature detection and classification model for sonar imagery. The adaptability of the proposed model is rigorously assessed through cross-validation methods. To ascertain the effectiveness of the model, the sonar images are first denoised, followed by evaluating the accuracy of sonar image classification and the prediction of sonar signals across various algorithmic models. The findings indicate that the multi-feature fusion approach yields a classification accuracy ranging between 86% and 91%, surpassing other evaluated algorithms. Furthermore, the sonar signal curve predicted by the CNN algorithm more closely approximates the actual sonar signal curve compared to alternative methods. These results affirm that the deep learning-based CNN algorithm significantly enhances the accuracy of sonar image detection and classification.