A Cost-Effective Autonomous Beach Cleaning Rover Using Deep Learning

This work aims to transform beach garbage management by developing an autonomous rover that utilizes deep learning and computer vision. The main goal is to enable the rover to traverse coastal environments on its own while using its sophisticated onboard camera to identify various waste products. The rover will identify different sorts of rubbish by employing the YOLO algorithm that uses the Bounding Box Regression technique to improve waste management tactics. The work involves hardware design, neural network training, real-time computer vision integration, and extensive field testing. The hardware components of the rover are carefully engineered through iterative development, guaranteeing smooth mobility and efficient data collection. By incorporating real-time computer vision techniques, the rover will be able to evaluate visual data and make decisions that will help identify waste. In order to verify the project’s effectiveness in tackling beach pollution field testing is carried out to confirm the rover’s accuracy in navigation and waste detection. An autonomous rover that embodies technical innovation for long-term, environmentally sustainable preservation is successfully developed.