Underwater image recognition based on improved EfficientNet
An improved Efficientnet image recognition model was proposed to solve the problems of fuzzy details,multi-scale and large computing resources for underwater images.The initial model parameters were obtained by training the model on the open data set through transfer learning.Adaptive Parametric ReLU(APRelu)and Selective Kernel Network(SK)based modules are proposed to enhance the processing of image detail features and multi-scale problems.By keeping the first Layer in all MBConv6 modules and embedding the BN and APRelu module after the last MBConv6 module,it speeds up its convergence and removes redundant features.Improve model performance by using data enhancement,10-fold cross-validation,snapshot integration and other strategies.The experimental comparison shows that the accuracy of the mod-el on the test set reaches 97.32％,which is 3.75％percentage points higher than before the improvement,and has high recog-nition performance.
NETL
NSTL
万方数据
