Research on underwater fine-grained target recognition method based on multiscale feature fusion network model
The complex underwater environment and lighting conditions pose serious challenges to the recognition of fine-grained underwater targets.On the basis of optical sensor data and infrared sensor data,this article proposes an underwater target recognition method based on multi-scale feature fusion using multimodal information fusion technology.Firstly,the multimodal features of underwater targets are fused at three scales.In order to reduce the information loss of underwater multimodal features,low-level multi-modal features are fused to obtain target edge details.Considering the significant differences between targets caused by the complex underwater environment,intermediate multimodal features are fused to improve feature expression ability and accuracy.In order to utilize rich semantic information,a collaborative attention mechanism is introduced to model the pixel level correlation of high-level features and extract fine-grained underwater target category information.Secondly,in the decoder,the fused feature representations at different scales are gradually upsampled,and fused to further enhance the accuracy and stability of feature expression.Finally,the feature representation is compared with the label feature to obtain pixel level classification results.By comparing and analyzing the experimental results of single-mode and muitimodal underwater data,it is verified that the proposed method has achieved better recognition performance in underwater target recognition tasks.
万方数据
维普
