Deep learning-based object detection for smart solid waste management system - Meron Desta; Tagel Aboneh; Bisrat Derebssa (Annals of Environmental Science and Toxicology - Peertechz Publications, 2023)
Currently in Ethiopia, pollution and environmental damage brought on by waste increased along with industrialization, urbanization, and global population levels. Waste sorting, which is still done improperly from the household level to the final disposal site, is a prevalent issue. Real-time and accurate waste detection in image and video data is a crucial and difficult task in the intelligent waste management system. Accurately locating and classifying these wastes is challenging, particularly when there are various types of waste present. So, a single-stage YOLOv4-waste deep neural network model is proposed. In this study, a deep learning algorithm for object detection using YOLOv4 and YOLOv4-tiny is trained and evaluated. A total of 3529 waste images are divided into 7 classes, which include, cardboard, glass, metal, organic, paper, plastic, and trash. Each model uses three various inputs throughout the testing phase, including input images, videos, and webcams. Experiments with hyper-parameters on subdivision values and mosaic data augmentation were also done in the YOLOv4-tiny model. The outcome demonstrates that YOLOv4 performs better than YOLOv4-tiny for object detection specifically for waste detection. The outcome shows that YOLOv4 performs better than YOLOv4-tiny for object detection, even if YOLOv4-tiny's scores are higher in terms of computing speed. The best results from the YOLOv4 model reach mAP 91.25%, precision 0.91, recall 0.88, F1-score 0.89, and Average IoU 81.55%, while the best YOLOv4-tiny results are mAP 82.02%, precision 0.75, recall 0.76, F1-score 0.75, and Average IoU 63.59%. This research also proves that the models with smaller subdivision values and using a mosaic have optimal performance.