Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model
Mosharof Hossain Dipo, Fahmid Al Farid, Md. Sifti Al Mahmud, Muntasir Momtaz, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim
Abstract
Increased waste volume and limitations of traditional separation methods have made waste management a hot topic in recent years. To enable the recycling process to be optimized and to minimize environmental impact, waste materials must be well detected and classified. Building on this research, the system is an automated waste-detecting system that integrates machine vision and artificial intelligence (AI). It is coupled with advanced convolutional neural networks (CNNs), which are used for data collection, real-time waste detection, and classification of the proposed framework. Images of waste were captured in many different settings and analyzed with a YOLOv12-based model. The system achieves more gain in detecting and categorizing waste types with 73% precision and a mean average precision (mAP) of 78% in 100 epochs. Results indicate that the YOLOv12 model surpasses the current detection algorithms to provide an efficient and scalable solution to waste management challenges.