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Benthic Organism Intelligent Recognition System

Benthic Organism Intelligent Recognition System

summary：Deep integration of convolutional neural network (CNN) AI technology with years of research achievements in specialized fields has enabled rapid and highly accurate intelligent identification of benthic organisms. This system simulates the human visual neural processing mechanism, using a hierarchical structure of convolutional layers, pooling layers, and fully connected layers to perform multi-scale feature analysis of benthic organism images. Based on training with massive biological samples, the network architecture can autonomously learn subtle key distinguishing features among species. Through feature space mapping, pattern matching, and probabilistic classification analysis processes, the system's overall identification accuracy is significantly improved. Additionally, it has strong adaptability and generalization capabilities; by introducing adaptive optimization algorithms, the model exhibits robust performance in complex environments and supports incremental learning and identification of unlabeled biological samples.

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Deeply integrating convolutional neural network (CNN) AI technology with years of research achievements in specialized fields, this system achieves rapid and highly accurate intelligent identification of benthic organisms. The system simulates the human visual neural processing mechanism, using a hierarchical structure of convolutional layers, pooling layers, and fully connected layers to perform multi-scale feature analysis of benthic organism images. Based on training with massive biological samples, the network architecture can autonomously learn subtle key distinguishing features among species. Through feature space mapping, pattern matching, and probabilistic classification analysis processes, the system significantly improves overall identification accuracy. Additionally, it possesses strong adaptability and generalization capabilities; by introducing adaptive optimization algorithms, the model demonstrates robust performance in complex environments and supports incremental learning and identification of unlabeled biological samples.

The operation is simple and highly intelligent. The system interface is clean, well-organized, and easy to operate, with control functions visualized and intuitive, enabling one-click intelligent recognition.

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Benthic Organism Intelligent Recognition System

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