Reliable Fill-Level Monitoring of Recycling Glass Containers

Optimization of waste collection is an important component in achieving sustainable solid waste management for modern cities. A basic prerequisite for route optimization is, naturally, information about the current fill levels of the container. This paper proposes a novel method for reliable monitoring of fill levels in glass containers. A data-based, hybrid approach was taken, by using vibration data recorded from an accelerometer mounted on each container. Firstly, a deep learning model is used to estimate the mean and variance of the current fill level. Two types of Bayesian neural networks and one ensemble- based model have been compared for this purpose. The final fill-level estimate is computed by a discriminative Kalman filter, improving estimation quality and reducing variance by fusing the instantaneous estimates with a simple, linear statistical model of the filling process. The proposed method was tested on two large real-world datasets with over 200 containers and 300,000 recorded samples. Because of the dataset imbalance, the models were optimized using label distribution smoothing. The final results demonstrate improvements across all fill levels, with the most significant enhancement observed in the estimation of the especially important and difficult case of higher fill levels, where the estimation error was reduced by up to 60%.