A High-similarity shellfish recognition method based on convolutional neural network

Abstract

The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish recognition. This study proposes a new shellfish recognition method FL_Net based on a Convolutional Neural Network (CNN). We first establish the shellfish image (SI) dataset with 68 species and 93 574 images, and then propose a filter pruning and repairing model driven by an output entropy and orthogonality measurement for the recognition of shellfish with high similarity features to improve the feature expression ability of valid information. For the shellfish recognition with unbalanced samples, a hybrid loss function, including regularization term and focus loss term, is employed to reduce the weight of easily classified samples by controlling the shared weight of each sample species to the total loss. The experimental results show that the accuracy of shellfish recognition of the proposed method is 93.95%, 13.68% higher than the benchmark network (VGG16), and the accuracy of shellfish recognition is improved by 0.46%, 17.41%, 17.36%, 4.46%, 1.67%, and 1.03% respectively compared with AlexNet, GoogLeNet, ResNet50, SN_Net, MutualNet, and ResNeSt, which are used to verify the efficiency of the proposed method.

Keywords

• Shellfish recognition
• High similarity
• Unbalanced samples
• Convolutional Neural Network
• Filter pruning and repairing
• Hybrid loss function