Smart Floating Waste Collector
Dr.Komma Lavanya1, Mukala swathi2, Tamada Kusumanjali3, Chittimalla venu kumar 4, Althi vamsi 5, kadiyala jaysree6
1Department of Electrical and Electronics Engineering, Anil neerukonda Institute of Technology and Sciences Visakhapatnam, India
2Department of Electrical and Electronics Engineering, Anil neerukonda Institute of Technology and Sciences, Vishakapatnam, India
3Department of Electrical and Electronics Engineering, Anil neerukonda Institute of Technology and Sciences, Vishakapatnam, India
4Department of Electrical and Electronics Engineering, Anil neerukonda Institute of Technology and Sciences, Vishakapatnam, India
5Department of Electrical and Electronics Engineering, Anil neerukonda Institute of Technology and Sciences, Vishakapatnam, India
6Department of Electrical and Electronics Engineering, Anil neerukonda Institute of Technology and Sciences, Vishakapatnam , India
Abstract— Water pollution due to floating solid waste in rivers, lakes, and coastal areas has become a significant environmental concern. This paper presents an experimental smart floating waste collector that integrates deep learning–based object detection with an automated collection mechanism. A USB camera captures images of the water surface, which are processed using the YOLO (You Only Look Once) algorithm to detect waste materials such as plastic bottles, polythene bags, and aluminium cans. Upon detection, the output is transmitted to an Arduino Uno microcontroller, which activates a DC motor–driven conveyor system and a servo mechanism for waste collection. Additional sensors, including ultrasonic and inductive proximity sensors, enhance obstacle detection and system awareness. The system is powered by solar energy unit with a battery, enabling continuous operation in outdoor environments.
Experimental evaluation shows effective real-time detection and reliable mechanical operation. As a prototype model, the system demonstrates the feasibility of combining deep learning with automated waste collection for sustainable aquatic waste management.
Keywords— Deep Learning, YOLO, Object Detection, Floating Waste Detection, Arduino, Solar-Powered System, Computer Vision, Smart Waste Management
