ACCESSIBLE-AND-AUTOMATIZED-RECYCLING-SYSTEM-USING-COMPUTER-VISION-TECHNIQUES

With the increasing population and the effect of uncontrolled urbanization, the damage caused by humanity to nature is getting worse. One of the biggest factors that contribute to this destructive effect is the solid waste problem. Although the most important solution to overcome this problem is recycling, the proposed solutions are not sufficiently automated nor are they accessible. For instance, in 2016, approximately 2,000,000,000,000 kg of solid waste was produced by humans. Even though, recycling is the best remedy for that problem, the proposed solutions are neither automatized nor accessible enough. They usually rely on human effort and require improvement in terms of work force and cost. In the recycling stage, hardest task is classifying the materials according to their type. With recent progress in computer vision, it is possible to come up with a more reachable, technologically advanced solution which also minimizes the human error. In this study, we propose a framework that is an alternative to expensive solid waste classification approaches. In the proposed method, a thermal camera and new generation computer vision techniques are used to classify the material regardless of its colour and texture. Material to be classified is heated using a light source, then left for cooling while it is being observed by thermal camera. Images obtained from thermal camera are enhanced using image processing techniques, and their descriptive features are extracted. Then, these features are evaluated and the material is classified. In this way, it is aimed to achieve an automated accessible system, which would both save manpower and be financially economical. The success of our system is calculated and presented on a data set we collected in our lab.