Design of a system for the detection and monitoring of falling waste

Nowadays, waste management is inefficient because of the inadequacy of the waste differentiation and recycling process. This issue causes an increase in costs in economic and especially in environmental terms. Indeed, since it is more economically convenient to produce new objects by using raw materials instead of recycled ones, the waste continues to increase, and so do the polluting emissions of carbon dioxide. This thesis work has been carried out in collaboration with ReLearn, an innovative Italian startup whose mission is to optimize the waste treatment process and consider waste no longer a problem but a resource. To simplify recycling and improve waste management, they have developed a smart bin called Nando that is able to automatically differentiate all the waste inserted inside it. By using robotics and artificial intelligence, Nando recognizes the material of which the waste is composed and then sorts it into the correct bin. This thesis project aims to design a system to estimate the volume of objects placed inside Nando. Volume measurements can be useful to monitor the bin fill level and detect possible objects that get stuck falling into the appropriate container. In order to avoid adding additional hardware that would entail additional costs, the measurement has been performed using only the camera already present inside the bin that was used to recognize the waste material. Taking advantage of the recent progress in the field of depth estimation achieved by deep learning methods, it has been possible, starting from a single RGB image of the object captured inside the bin, to predict the corresponding depth image. The Deep Convolutional Neural Network (DCNN) used to estimate depth has been trained on a dataset specifically built for the purpose of the thesis. Subsequently, from the depth image obtained in the previous step, the volume of the waste has been computed with an estimation algorithm specifically developed. Finally, the outcome is a system able to estimate the volume of an object starting exclusively from an RGB image that portrays it. The results of the performed simulations show good scalability of the algorithm and reliable estimation results despite using only a simple camera.