A Hybrid Deep Learning Model for Waste Detection and Classification Utilizing YOLOv8 and CNN

This research presents a new hybrid deep-learning model for object identification and classification. The model combines the excellent classification performance of a Convolutional Neural Network (CNN) with the strong detection capabilities of YOLOv8. The suggested approach generates bounding boxes that indicate probable objects, using YOLOv8 for initial object localization. Subsequently, CNN further enhances these detections by categorizing the recognized items into compost and non-compost. This dual-phase technique enhances detection accuracy and classification precision, successfully tackling common obstacles encountered in real-world visual identification tasks, such as intricate backgrounds and diverse item sizes. The effectiveness of the hybrid model is evaluated using a specialized dataset related to waste recycling, which demonstrates significant improvements compared to individual models. The findings indicate that the hybrid YOLOv8 and CNN model attains an F1 score of 0.86, precision of 0.85, recall of 0.87, and accuracy of 0.88, surpassing the performance of both YOLOv8 and CNN models. The findings indicate that this comprehensive method provides a hopeful resolution for tasks that require the precise and effective identification of objects, thereby making it a valuable addition to computer vision and deep learning. The research analyses the model’s structure, the training process, and the experimental outcomes, highlighting the advantages of combining YOLOv8 and CNN approaches to enhance the overall performance in tasks related to object recognition and classification.