MCCA-Net: Multi-color convolution and attention stacked network for Underwater image classification

Abstract

Underwater images are serious problems affected by the absorption and scattering of light. At present, the existing sharpening methods can't effectively solve all underwater image degradation problems, thus it is necessary to propose a specific solution to the degradation problem. To solve the above problems, the Multi-Color Convolutional and Attentional Stacking Network (MCCA-Net) for Underwater image classification are proposed in this paper. First, an underwater image is converted to HSV and Lab color spaces and fused to achieve a refined image. Then, the attention mechanism module is used to fine the extracted image features. At last, the vertically stacked convolution module fully utilizes different levels of feature information, which realizes the fusion of convolution and attention mechanism, optimizes feature extraction and parameter reduction, and improves the classification performance of the MCCA-Net model. Extensive experiments on underwater degraded image classification show that our MCCA-Net model and method outperform other models and improve the accuracy of underwater degraded image classification. Our image fusion method can achieve 96.39% accuracy on other models, and the MCCA-Net model achieves 97.38% classification accuracy.