吉林大学学报（信息科学版）2023，Vol.41Issue(6)：1030-1040,11.
面向垃圾图像分类的残差语义强化网络
Network of Residual Semantic Enhancement for Garbage Image Classification
摘要
Abstract
In order to better protect the ecological environment and increase the economic value of recyclable waste,to solve the problems faced by the existing garbage identification methods,such as the complex classification background and the variety of garbage target forms,a residual semantic enhancement network for garbage image classification is proposed,which can strip foreground semantic objects from complex backgrounds.Based on the backbone residual network,the network uses visual concept sampling,inference and modulation modules to achieve visual semantic extraction,and eliminates the gap between semantic level and spatial resolution and visual concept features through the attention module,so as to be more robust to the morphological changes of garbage targets.Through experiments on the Kaggle open source 12 classified garbage dataset and TrashNet dataset,the results show that compared with the backbone network ResNeXt-50 and some other deep networks,the proposed algorithms have improved performance and have good performance in garbage image classification.关键词
模式识别与智能系统/垃圾分类/视觉概念/视觉采样/概念推理/注意力机制Key words
pattern recognition and intelligent system/garbage classification/visual concept/visual sampling/concept reasoning/attention mechanism分类
信息技术与安全科学引用本文复制引用
苏雯,徐鑫林,胡宇超,黄博涵,周佩廷..面向垃圾图像分类的残差语义强化网络[J].吉林大学学报（信息科学版）,2023,41(6):1030-1040,11.基金项目
国家自然科学基金资助项目(62006209) （62006209）
