About this document
Deep Learning for Waste Classification by dien15082005kg is a document available to read on EtoBox.
This paper presents a robust deep learning-based approach to enhance waste sorting and recycling efficiency, utilizing a dataset of 10,406 images across 28 recyclable waste categories. The proposed dual-stream network achieved an overall classification accuracy of 83.11%, while the GELAN-E model for object detection attained a mean average precision of 63%, outperforming existing models. These advancements demonstrate significant progress in intelligent waste management, addressing the challenges of traditi
- Author
- dien15082005kg
- Language
- EN