Research on marine fish classification and recognition based on an optimized ResNet50 model

ObjectiveIn order to solve the problems of low accuracy and limited generalization ability in traditional marine fish species identification methods, the optimized ResNet 50 model is proposed in this paper.MethodsFirst, a data set of marine fish images was constructed, targeting 30 common marine fish species (e.g., Japanese Eel Anguilla japonica, Japanese Horsehead Branchiostegus japonicus, Black Sea Sprat Clupeonella cultriventris, and Atlantic Cutlassfish Trichiurus lepturus). The marine fish images were pre-processed to increase the sample size of the data set. Second, the ResNet50 model was optimized by introducing a Dual Multi-Scale Attention Network (DMSANet) module to improve the model's attention to subtle features. A dropout regularization mechanism and dense layer were added to improve the model's generalization ability and prevent overfitting. The triplet loss function was adopted as the optimization objective of the model to reduce errors. Third, species identification was conducted on 30 species of marine fish to test the comprehensive performance of the optimized ResNet50 model.ResultThe test results showed that the optimized model had a recognition accuracy of 98.75% in complex situations, which was 3.05% higher than that of the standard ResNet50 model. A confusion matrix of the visual analysis results showed that the optimized ResNet50 model had a high accuracy rate for marine fish species recognition in many cases. To further validate and evaluate the generalization ability of the optimized ResNet50 model, partial fish data from the ImageNet database and the Queensland University of Technology (QUT) Fish Dataset were used as data sets for performance experiments. The results showed that the optimized ResNet50 model achieved accuracies of 97.65% and 98.75% on the two benchmark data sets (ImageNet and the QUT Fish Dataset, respectively).ConclusionThe optimized ResNet50 model integrates the DMSANet module, effectively capturing subtle features in images and improving the accuracy of fish classification tasks. This model has good recognition and generalization abilities in complex scenes, and can be applied to marine fish recognition tasks in different situations.