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Smart Floating Waste Collector
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Researchers built a solar-powered floating robot that uses YOLO-based deep learning to detect plastic bottles, polythene bags, and aluminum cans on water surfaces in real time and automatically collects them via a motorized conveyor, demonstrating feasibility as a low-maintenance aquatic waste management prototype.
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
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Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.