Classification Of Solid Waste Using Computer Vision Techniques

In recent years, waste management and need for recycling has gained importance more than ever due to the increase in population. Because of this reason, making recycling easily applicable and available with reasonable cost is very crucial. Mainstream recycling methods rely on human effort hence there is a room for automatization. The most potentially improvable step of the recycling is the classification of materials in which solid wastes are classified with respect to their materials. In this work, an alternative method to classification step in mainstream recycling is proposed. Proposed method includes an active heating unit that consists of 3 thermal lamps. It also includes a FLIR T420 thermal camera. Objects to be classified with respect to their material are heated using the heating unit, then left for passive cooling. The process is observed using the thermal camera. Obtained images are then used in machine learning methods for classification. The proposed method aims to automatize the classification step in recycling as well as making it cheaper and easily applicable. The accuracy of the system is calculated in the dataset that is collected in our lab.