| dc.description.abstract | Environment-related problems have become increasingly hazardous towards humans and
pose a risk to the ecosystem, one of which is the production of abundant trash. Despite many
solutions, trash recycling still meets several difficulties. This thesis introduces a solution of
using two convolutional neural network models to help classify five types of household trash
(cardboard, paper, glass, fabric, metal) through images and employing a YOLOv8 model for
object segmentation. The main predictor model is MobileNetV3Large, chosen for its fast
performance and good overall accuracy. The support model is EfficientNetV2B0, used for
boosting the system’s overall prediction confidence. The training dataset is combined from
several open-source online datasets with custom augmentation techniques applied. Grad-CAM
and t-SNE algorithms are employed to assess the effect of custom augmentation. The model
achieves 94% accuracy with fast inference time and good stability, enabling faster trash sorting
for better recycling efficiency. | en_US |
