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. 

Key words: pattern recognition and intelligent system, garbage classification, visual concept, visual sampling, concept reasoning, attention mechanism