Automated Curbside Waste Collection using Advanced Computer Vision Strategies
Computer vision is an interesting and rapidly growing area of study that is often used in conjunction with artificial intelligence (AI) methods. The number of useful applications for computer vision techniques is vast, and includes object detection, analysis of human actions, autonomous vehicles, and target tracking. A local company has partnered with the Intelligent Control and Estimation Laboratory to work on further developing an automated waste collection system. Currently, the system utilizes an industrial-grade camera, mechatronics control box that integrates with waste collection trucks (e.g., robot arms), and customized AI software. The system relies heavily upon its ability to successfully and accurately detect a waste collection cart (e.g., garbage, recycling, or compost bin). The current methodology employs a significant amount of manual annotation or labelling of pictures to train the system to detect a cart; all of which is time consuming and costly. This research thesis implemented and further developed convolutional neural networks (CNN) and ‘you only look once’ (YOLO) object detection strategies. The application of CNN and YOLO to the automated waste collection system improved successful cart detection rates (about 98%), reduced false positives (less than 2%), and will improve operational safety and reliability when implemented in the real-time system.