Marine organism classification method based on hierarchical multi-scale attention mechanism

作     者：XU Haotian CHENG Yuanzhi ZHAO Dong XIE Peidong 

作者机构：School of Information Science and Technology， Qingdao University of Science and Technology 

出 版 物：《Optoelectronics Letters》 (光电子快报(英文))

年 卷 期：2024年

学科分类：070703[理学-海洋生物学] 07[理学] 0707[理学-海洋科学] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China (No. 61806107) the National Natural Science Foundation of China (No.61702135) 

摘      要：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 EfficientNetV2 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 generalization ability. Lastly， Focal Loss is introduced to adjust the weights of complex samples to address the issue of imbalanced 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.