集美大学学报（自然科学版）2026，Vol.31Issue(2)：217-223,7.DOI:10.19715/j.jmuzr.2026.02.09
基于改进MobileNetV3-Small网络的灰度图像分类方法
Gray-Scale Image Classification Based on Improved MobileNetV3-Small
摘要
Abstract
Aiming at the channel attention limitation of the SE module in the MobileNetV3 network,an im-age classification method based on SE-SAM Fusion attention mechanism is proposed.The original image was amplified using background uniformity,clipping,and scaling strategies,compared with lightweight models such as ResNet18,ShuffleNetV2,and EfficientNet,and ablation experiments were performed to verify the effective-ness of the improved module.The results indicate that the improved MobileNetV3-Small network achieves an average accurance of 96.64%on the mixed-enhanced dataset with a loss value of 0.015 8,marking a 0.006 4 reduction from its initial model.The model's weight file is 6.2 MB,which is approximately 1/7 of that of the similar lightweight model ResNet18 and 1/4 of that of EfficientNet,respectively.关键词
图像分类/MobileNetV3-Small/注意力机制/迁移学习Key words
image classification/MobileNetV3-Small/attention mechanism/transfer learning分类
信息技术与安全科学引用本文复制引用
叶雨婕,胡志超,刁龙,邹蕾,王云超,林少滨..基于改进MobileNetV3-Small网络的灰度图像分类方法[J].集美大学学报（自然科学版）,2026,31(2):217-223,7.基金项目
国家自然科学基金面上项目子课题(52375046) （52375046）
福建省自然科学基金项目(2021J01852) （2021J01852）
