Marine Organism Classification Method based on Hier-archical Multi-Scale Attention Mechanism

Abstract:

We propose a hierarchical multi-scale attention mechanism-based model in response to the low accuracy and inefficient manual classification of existing oceanic biological image classification methods. Firstly, the H-EMA module is designed for lightweight feature extraction, achieving outstanding performance at a relatively low cost. Secondly, an improved Ef-ficientNetV2 Block is used to integrate information from different scales better and enhance inter-layer message passing. Furthermore, introducing the CBAM module enhances the model's perception of critical features, optimizing its generali-zation ability. Lastly, Focal Loss is introduced to adjust the weights of complex samples to address the issue of imbal-anced categories in the dataset, further improving the model's performance. The model achieved 96.11% accuracy on the intertidal marine organism dataset of Nanji Islands and 84.78% accuracy on the CIFAR-100 dataset, demonstrating its strong generalization ability to meet the demands of oceanic biological image classification.

Key words:Deep learning, Image classification, Marine organisms, Attention mechanism