Detecting glass and metal in consumer trash bags during waste collection using convolutional neural networks.

We present a proof-of-concept method to classify the presence of glass and metal in consumer trash bags. With the prevalent utilization of waste collection trucks in municipal solid waste management, the aim of this method is to help pinpoint the locations where waste sorting quality is below accepted standards, making it possible and more efficient to develop tailored procedures that can improve the waste sorting quality in areas with the most urgent needs. Using trash bags containing various amounts of glass and metal, in addition to common waste found in households, we use a combination of sound recording and a beat-frequency oscillation metal detector as inputs to a machine learning algorithm to identify the occurrence of glass and metal in trash bags. A custom-built test rig was developed to mimic a real waste collection truck, which was used to test different sensors and build the datasets. Convolutional neural networks were trained for the classification task, achieving accuracies of up to 98%. These promising results support this method's potential implementation in real waste collection trucks, enabling location-specific and long-term monitoring of consumer waste sorting quality, which can provide decision support for waste management systems, and research on consumer behavior.