A Waste Classification model in Low-illumination scenes based on ConvNeXt

Waste classification is an essential part of environmental pollution management in modern society. Object detection is an accurate and efficient way to classify waste, which is conducive to recycling resources. However, due to low object discriminability, existing waste classification models cannot classify waste in low-illumination scenes. A waste classification model, Dark-Waste, is designed to classify wastes in a low-illumination scenario. Firstly, to solve the scarcity of training data, an efficient and low-cost Illumination Conversion method is proposed to generate the low-light image. Secondly, the improved ConvNeXt network is combined with YOLOv5 to accurately and efficiently classify waste. Finally, we validated the model on a self-built dataset in real scenarios. The experimental results show that Dark-Waste achieves the best detection performance in low-illumination scenes. The Dark-Waste provides a new approach to waste management in complex environments and effectively contributes to the sustainable development of the urban ecological environment.

Automated summary

Waste classification is an essential aspect of environmental pollution management in modern society. However, existing waste classification models struggle with low-illumination scenes. This study proposes Dark-Waste, a waste classification model, that effectively addresses these challenges and achieves accurate classification in low-light environments. The model combines illumination conversion and an improved ConvNeXt network with YOLOv5, and it has been validated on a self-built dataset. The results demonstrate the model's superior detection performance in low-illumination scenes, highlighting its significance in waste management in complex environments.