Multi-group convolutional neural network for gender recognition of Portunus tritubereulatus

High-precision intelligent recognition and classification has become a key technology for intelligent sorting of Portunus trituberculatus. We first preprocessed and enhanced the collected images of P. tritubereulatus so as to build a Portunus gender classification dataset (PGCD). Besides, we proposed a multi-group convolutional neural network for gender classification of P. tritubereulatus, mainly using ResNet50 to extract features from image patches, thereby reducing information loss during the feature extraction process. In order to focus more on finding useful information of input data, we also constructed an attention mechanism before gender classification to emphasize the importance of details in the global feature map. The results show that the classification accuracy, recall and accuracy of this method on PGCD were 95.59%, 94.41% and 96.68%, respectively, with a recognition error rate of only 4.41%. It is concluded that the method has superior classification performance and can be used in automatic classification and recognition systems for Portunus gender.