Artificial Intelligence in Automated Sorting in Trash Recycling - Costa, Bernardo S.; Bernardes, Aiko C. S.; Pereira, Julia V. A.; Zampa, Vitoria H.; Pereira, Vitoria A.; Matos, Guilherme F.; Soares, Eduardo A.; Soares, Claiton L.; Silva, Alexandre F. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2018)
A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy around 93%.