Garbage Detection System Using Robot and Advanced YOLO Algorithm

Escalating urbanization has exacerbated challenges in waste management, demanding innovative solutions. This study introduces a smart waste management system integrating the YOLOv5-OCDS (Object Counting Detection System) with TensorFlow and IoT technologies. The YOLOv5-OCDS algorithm, trained on a diverse dataset, efficiently categorizes paper, cardboard, glass, metal, and plastic waste, ensuring rapid and precise results with minimal background noise. The system utilizes a camera module and servomotor on a Raspberry Pi 4 for efficient waste sorting. An ultrasonic sensor monitors bin fill levels, and GPS technology provides real-time location data. The LoRa(Long Range) module transmits bin status at 915 MHz, enhancing remote monitoring. Electronic work is secured by Radio Frequency Identification(RFID)-dependent locker accessible only with authorized badges for maintenance. The system, framed around robotics, neural networks, IoT, and deep learning, demonstrates superior speed and accuracy in garbage detection compared to existing methods. YOLOv5-OCDS’s real-time prediction capability streamlines detection, offering precise results with minimal background noise and exceptional learning capabilities. This research contributes to efficient waste management in urban areas, addressing the escalating challenges of increasing garbage output.