# An Energy Saving Road Sweeper Using Deep Vision for Garbage Detection
## 1 Introduction  
Sweeping machines are a broad range of trucks tasked with the clean-up of most city streets and squares. They operate collecting garbage, gravel, trash, grass, dirt and leafs from the roads and the sidewalks. The main sweeping tools of such trucks are a variable number of rotating brushes, with the job of sweeping the road floor and \"lifting\" trash for the consecutive collection. A vacuum and/or conveyor belt will carry the collected garbage inside an internal bin for opportune disposal. These brushes and the belt have different settings, such as pressure on the floor, speed of rotation and speed of the belt, each tailored to a specific type of trash and, therefore, they need an accurate maneuvering for best performance. Moreover, these components are often very energy hungry, since cleaning very dirty road pavements may involve strong pressures and speeds. Besides the movement of the vehicle, these cleaning tools are the most energy-absorbing parts of these small trucks. All of these systems are in fact powered by combustible fuel, or by an electric battery. In either case, the cleaning routine (driving the vehicle around the city) is performed by a human operator, and, as such, a typical usage scenario is a workday of eight hours of cleaning for these sweepers. Especially in the case of battery powered cleaners, this poses some constrains on the energy used by the vehicle, as every second of battery should be preserved in order to complete a daily routine without recharging/refueling the vehicle. Another problem with these machines, operating in hard, real-life conditions from all over the world, is the wear-and-tear of its components. Many parts of these sweeper machines, especially the brushes, need to be replaced every month or so. This is both a huge cost and an additional source of waste material. Fortunately, most of these energy efficiency problems can be solved by an accurate operation of the cleaning tools. The first power/weariness saving procedure is not cleaning where there is not any trash. Other power saving options are applying less brush pressure in case of \"light\" litter (e.g., leaves) as opposed to more difficult waste (e.g., bottles, mud). Both of these proposals are unfortunately left as the responsibility to the truck pilot, who may be or may be not trained well for such operations, and would need to invest much of his/her attention to those fine regulation tasks, diverting it from other important tasks (e.g., driving). The usual consequence of such considerations is that often these machines are operating in an \"always on\"/\"full cleaning\" power setup, wasting energy, money and creating waste, noise and pollution. Therefore, the need of an automated artificial intelligence system for these tasks arises. Having a computer automatically adjusting these settings would be valuable from both an economic and an ecological standpoint. The scope of this paper is to create an artificial intelligence system mounted on-board on the vehicle, able through a camera (eye) and a neural network (brain) to correctly identify each single piece of garbage encountered on the streets (leaves, paper, gravel, bottles, cigarette stubs, etc.). Moreover, the system will be able to convert the trash locations from the image coordinates to 3D real-world coordinates, and to collect all of them automatically through the truck actuators (brushes and vacuum). Fortunately, nowadays cameras are quite cheap, performant and energy efficient, and the same can be said for embedded PC boards and GPUs. At the same time, deep vision technology has seen some mayor breakthroughs, and is now able to perform most visual recognition tasks with a human-like performance. The main challenge and contribution of this work is to verify if everything is ready to be put together in a real-time, cheap, energy saving system that can be implemented in a real truck.