重庆理工大学学报2024，Vol.38Issue(23)：155-164,10.DOI:10.3969/j.issn.1674-8425(z).2024.12.019
注意力机制和多尺度特征融合的细粒度图像分类
Attentional mechanisms and multiscale feature fusion for fine-grained image classification
摘要
Abstract
Fine-grained image classification is susceptible to background interference,inaccurate localization of key regions and a large number of model parameters.To address these problems,we propose a classification network with attention mechanism and multi-scale feature fusion.First,based on the YOLOv7 network,the lightweight backbone network is rebuilt using the Ghost BottleNeck module,and the Conv in the neck network is replaced with GhostConv to realize the lightweight of the model.Second,a parameter-free SimAM attention mechanism is introduced to infer the 3D attention weights of the feature map by considering the correlation between spatial and channel dimensions,characterizing locally salient features,suppressing useless features,and improving the effectiveness of the target region information.Finally,a feature-selectable pyramid pooling module is built to help the network model better capture and process the multi-scale features of the target and improve the model's perceptual ability.Our results suggest AM-Net on Stanford Dogs dataset reaches 88.9%in accuracy,83.6%in precision,85.7%in recall and 84.6%in F1 score.Moreover,the number of model parameters is 26.53 MB,and the frame rate per second reaches 89.3 frames per second on Stanford Cars dataset with 95.2%in accuracy,93.7%in precision and 94.9%in recall.Our experimental results show AM-Net improves the classification accuracy of fine-grained images and reduces the weight of the network,markedly improving the performance compared with other network models.关键词
人工智能/细粒度分类/特征提取/注意力机制/多尺度特征融合Key words
artificial intelligence/fine-grained classification/feature extraction/attention mechanisms/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
李云红,郭越,谢蓉蓉,张蕾涛,苏雪平,李丽敏,陈锦妮..注意力机制和多尺度特征融合的细粒度图像分类[J].重庆理工大学学报,2024,38(23):155-164,10.基金项目
国家自然科学基金项目(62203344) （62203344）
陕西省自然科学基础研究重点项目(2022JZ-35) （2022JZ-35）
陕西高校青年创新团队项目 （）
