7th Eurasia Waste Management Symposium, İstanbul, Turkey, 21 - 23 October 2024, pp.1-8
Effective waste management is essential
for safeguarding human health and ensuring a clean environment. A critical
component of waste management is the systematic separation of waste based on
its categories. In this study, the classification of wastes is carried out by
utilizing convolutional neural networks. Firstly, a novel dataset of 3035 waste
images that contains 7 different categories (battery, glass, metal, organic,
trash, paper-cardboard, plastic) is constructed by combining TrashNet image
dataset (2527 images) and our own dataset (508 images). Then, the dataset was
randomly divided to train (80% of data) and test (20% of data) sets to perform
the hold-out validation. Finally pre-trained convolutional neural network
ResNet50 is trained on the train set, and the model’s validity is checked by
comparison with the test set. It is shown that ResNet50 is able to classify the
wastes with high accuracy value (99.1%), even if the training is stopped at
comparatively low epoch numbers. Our results indicate that the wastes can
successfully be classified by deep learning from image data and the prevalent
future direction regarding this topic is the detection and classification of
wastes directly from videos in continuous processes.