Advanced Waste Classification Using Image Recognition and Deep Learning

The research aims to create an innovative waste categorization system. To successfully detect diverse sorts of waste items, we used cutting-edge deep learning techniques and image recognition algorithms. Transfer learning was used to improve the model’s performance by using pre-trained convolutional neural networks (Inception V3, GoogleNet, and MobileNet) and fine-tuning them on the waste classification dataset. Furthermore, data augmentation techniques enhanced and diversified the training data, boosting the model’s capacity to handle variances in waste photos. We found MobileNet to be the most efficient waste classification model after extensive model comparison and tuning. The proposed approaches, which included transfer learning, data augmentation, and model optimization, considerably improved the waste classification system’s accuracy and efficiency, demonstrating its potential for effective waste management and resource conservation.