Аңдатпа
This paper introduces the analysis and design of an intelligent waste sorting system with deep learning to analyze waste and increase sustainability in waste management. The dataset (2,790 images in 6 categories of waste, including plastic, paper, cardboard, glass, metal and organic waste) was gathered using custom methods and further preprocessed and augmented to train models. Three convolutional neural network designs, including YOLOv8, DenseNet169, and ResNet50v2, were trained and evaluated in terms of accuracy, precision, and recall and F1-score. DenseNet169 was most accurate (92% in total), and balanced in all evaluation measures, whereas YOLOv8 showed excellent real-time detection with 91% accuracy, which qualifies the latter to be utilized in the practice of waste sorting. The findings substantiate the claims that transfer learning and fine-tuning enhance the reliability of classification mostly on smaller datasets. The suggested AI-based system illustrates the ability of computer vision to adhere to the vision of scalable, efficient, and sustainable waste management through waste management solutions based on the intelligent and data-driven decision-making model.