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

为更好地保护生态环境并提高可回收垃圾的经济价值,针对现有的垃圾识别方法面临的分类背景复杂、垃圾目标形态变化多样等问题,提出一种面向垃圾图像分类的残差语义强化网络,能从复杂背景中剥离前景语义目标。该网络以骨干残差网络为基础,利用视觉概念采样、推理以及调制模块实现视觉语义的提取,并通过注意力模块消除语义层次和空间分辨率与视觉概念特征的差距,从而对垃圾目标形态变化更加具有鲁棒性。通过在Kaggle开源的12分类垃圾数据集及TrashNet数据集上进行实验,结果表明,相较于骨干网络ResNeXt-50和其他深层网络,该算法均取得了性能的提升,在垃圾图像分类任务上有较好表现。

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.