Monocular depth estimation for level assessment in an industrial waste management environment : A thesis within smart waste management

Abstract: With the transition to Industry 4.0, actors in many industries face challenges such as how to successfully implement technical solutions and retain competitiv eadvantages. In the smart waste management sector, many solutions have been presented in how to create effecient sensors but a practical way of comparing these solutions is non-existent. From research within Industrial Internet of Things (IIoT) and interviews with operators at Scania, we present a clear and effective way of comparing smart waste management sensors with regards to operational effectiveness. Along with this, we present a way to measure  fill volume of garbage containers using monocular depth estimation and compare this to using ultrasonic sensors. Our findings show that depth estimation with deep convolutional neural networks is viable as long as environmental conditions can be controlled, although, we have also found that ultrasonic sensors outperform depth estimation in many metrics and is the desired way of measuring fill level of containers in many applications. Despite this, the results of this research show promise in that depth estimation can be used in conjunction with object recognition models, leading to the obsolescence of ultrasonic sensors in more complex applications.