Abstract
The detection of recyclable garbage plays an important role in waste-to-energy and carbon neutrality. However, due to the complexity of waste accumulation in waste-to-energy plants, the current garbage grabbing algorithms have limited accuracy. In order to overcome the above problems and avoid waste of resources, a novel recyclable garbage detection algorithm and corresponding system are studied in this paper. The CenterNet model is optimized by feature fusion so that it can better extract the subtle features of garbage. The YOLO model and original CenterNet model are adopted for garbage detection, and the backbone of YOLO model is optimized with VGG and DenseNet. Based on it, a garbage detection system is designed and a recyclable garbage dataset is constructed. The validation results show that the algorithm proposed in this paper is efficient and valid.
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Funding
The project is financially supported by the Social Development Project of Jiangsu Key R&D Program (BE2022680), the Ministry of Industry and Information Technology of China (No. 2021-R-43), the National Natural Science Foundation of China (No.61972214) and Jiangsu Postdoctoral Science Foundation(1601039B).
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Cheng, X., Hu, F., Song, L. et al. A Novel Recyclable Garbage Detection System for Waste-to-energy Based On Optimized CenterNet With Feature Fusion. J Sign Process Syst 95, 67–76 (2023). https://doi.org/10.1007/s11265-022-01811-1
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DOI: https://doi.org/10.1007/s11265-022-01811-1