DeepSegRecycle: Deep Learning and ImageProcessing for Automated Waste Segregation and Recycling

Effective waste management is a critical issue for sustainable development, and automated waste segregation and recycling systems can potentially revolutionize how we handle waste. This study introduces DeepSegRecycle, a unique approach that autonomously separates garbage for recycling using deep learning techniques. In this study, the GoogleNet CNN model has been used to identify different types of garbage. We also provide the findings from our performance evaluation of the system, which show its great accuracy of 97.3% in processed models and 93.78% accuracy in real-life testing scenarios. The effectiveness of garbage classification was verified by testing 1000 picture data of each category, ensuring precision in the evaluation. Our study emphasizes the promise of deep learning and image processing techniques for solving the worldwide waste management problem. By lowering the quantity of the garbage that goes to landfills and encouraging the circular economy, DeepSegRecycle has the potential to improve the environment and human health significantly.