Recognition algorithm of marine single-cell algae based on deep learning VGG network
WANG Yuzheng1, CHENG Yuan2, BI Hai3, YU Qiuyu1, LIU Dan1*
1.Coloege of Information Engineering, Dalian Ocean University, Dalian 116023, China; 2.Offshore (Dalian) Ecological Development Company Limited, Dalian 116085, China; 3.Smart Vision Dalian Research Institute, Dalian 116085, China
Abstract: In order to better identify marine single-celled algae, a single-cell alga recognition algorithm is developed based on improved VGG16 network—AlgaeNet. This algorithm is derived from the traditional VGG network, reducing the number of convolution kernels, and adding BatchNormalization layer to accelerate the neural network. The convergence rate of the loss value during the training process and the increase rate of the prediction accuracy of the test set samples (Chlorellaovalis and Dictyochafibula Ehrenberg) are found to be better under the same experimental conditions, compared with the traditional VGG network and the AlexNet network, with prediction accuracy of 99.317%. Experimental results showed that the algorithm had better classification and recognition performance in the field of single-celled alga recognition, with accurate recognition of marine single-celled algae.
王羽徵, 程远, 毕海, 于秋玉, 刘丹. 基于深度学习VGG网络模型的海洋单细胞藻类识别算法[J]. 大连海洋大学学报, 2021, 36(2): 334-339.
WANG Yuzheng, CHENG Yuan, BI Hai, YU Qiuyu, LIU Dan. Recognition algorithm of marine single-cell algae based on deep learning VGG network. Journal of Dalian Ocean University, 2021, 36(2): 334-339.
2021, Vol. 36 
