A LIGHTWEITGH CONVOLUTIONAL NEURAL NETWORK-BASED METHOD FOR LOSSLESS IDENTIFICATION OF INDIVIDUAL TAKIFUGU RUBRIPES

In aquaculture, avoiding duplicate individual biomass estimation is an important prerequisite for achieving accurate fish biomass estimation, the key is to perform individual fish identification, while few relevant studies have been reported. In this paper, a lightweight convolutional neural network-based identification method for individual fish identity was proposed, which can achieve high accuracy identification of individual Takifugu rubripes without loss. Firstly, SOLOv2 model was used for foreground segmentation, and combined with the characteristics of the body size of Takifugu rubripes, the dataset generation and filtering were completed by the method of calculating the center of mass and Different Hash Algorithms; subsequently, the effectiveness of mainstream deep learning image classification backbone networks and different loss functions in Takifugu rubripes identity recognition were tested separately from multiple dimensions; following that, an optimal combination method for the lossless identification of individual identity of Takifugu rubripes was established based on MobileNet v2 backbone network coupled with Softmax Loss function. The results showed that the accuracy of the proposed method can reach 90.2%, which is better than other related mainstream methods (accuracy 73.6%—89.3%), and related research results will provide technical support for non-destructive identification of individual fish identity and accurate biomass estimation in recirculating water culture.