Abstract
Road sweepers are ubiquitous machines that help preserve our cities cleanliness and health by collecting road garbage and sweeping out dirt from our streets and sidewalks. They are often very mechanical instruments, needing to operate in harsh conditions dealing with all sorts of abandoned trash and natural garbage. They are usually composed of rotating brushes, collector belts and bins, and sometimes water or air streams. All of these mechanical tools are usually high in power demand and strongly subject to wear and tear. Moreover, due to the simple working logic often implied by these cleaning machines, these tools work in an “always on”/“max power” state, and any further regulation is left to the pilot. Therefore, adding artificial intelligence able to correctly operate these tools in a semi-automatic way would be greatly beneficial. In this paper, we propose an automatic road garbage detection system, able to locate with great precision most types of road waste, and to correctly instruct a road sweeper in order to handle them. With this simple addition to an existing sweeper, we will be able to save more than 80% electrical power currently absorbed by the cleaning systems and reduce by the same amount brush weariness (prolonging their lifetime). This is done by choosing when to use the brushes and when not to, with how much strength, and where. The only hardware components needed by the system will be a camera and a PC board able to read the camera output (and communicate via CanBus). The software of the system will be mainly composed of a deep neural network for semantic segmentation of images, and a real-time software program to control the sweeper actuators with the appropriate timings. To prove the claimed results, we run extensive tests onboard of such a truck, as well as benchmark tests for accuracy, sensitivity, specificity and inference speed of the system.
Highlights
Sweeping machines are a broad range of trucks tasked with the clean-up of most city streets and squares
We presented a full system of artificial intelligence able to highly contribute to the working routine of road sweepers
While other works may tackle similar domain toy examples, or, high cost solutions, this is the only work that provides a real world, fully integrated solution that can be applied with ease “in the wild” to gain immediate benefits
Summary
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. 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, they need an accurate maneuvering for best performance. 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
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