Underwater target recognition technology based on deep learning
In the complex underwater scene,the target object has the characteristics of different poses,occlusion and complex background,which poses a huge challenge to the feature extraction ability of convolutional network.Mask R-CNN algorithm also has the problem of poor feature extraction ability in the process of underwater target feature extraction,which leads to poor accuracy of the algorithm in underwater target detection.Therefore,this paper proposes an improved underwa-ter target recognition method based on Mask R-CNN.First,use the pyramid segmentation attention module PAS to replace 3×3 module in ResNet50.This module first segments the channel,and then extracts the spatial information on each segmen-ted channel feature map without scale features.At the same time,it uses a more efficient ECANet channel attention module to replace the SENet channel attention module in PAS,and recalibrates the multi-dimensional channel attention weight;Fi-nally,the network structure of feature pyramid FPN is improved to strengthen the information fusion between different fea-ture layers.According to the experimental comparison in different scenes,the improved network can improve the accuracy of underwater target recognition,and the average detection accuracy can reach 91.3％.The improved Mask R-CNN network model proposed in this paper can adapt to complex and changeable underwater scenes,providing a theoretical basis and tech-nical solution for underwater target recognition.
万方数据
维普
