In a world where mounting environmental challenges, wireless technology enables innovative solutions. The Real-Time Material Classification System revolutionizes industrial waste management through automated sorting with AI of 99.2% accurate identification of plastics, glass, metals, and more, with real-time feedback. In IoT environments, an infrared sensor detects waste, a camera captures images, and a Raspberry Pi 4B processes data to relay signals to PLCs for precise separation. Wi-Fi integration streams live statistics to the Blynk app for cordless remote monitoring, reducing labor, enhancing recycling efficiency, and enabling early fault detection.

Invention Technical Description

When an object enters the sensor's detection range, the infrared sensor senses the object and triggers a signal to initiate the subsequent recognition process. As the central processing unit of the system, the Raspberry Pi 4B is responsible for running the entire object recognition system. It receives signals from the infrared sensor, processes the image data, and controls other components (such as the relay module) to respond to objects made of different materials. The camera module is used to capture images of objects on the conveyor belt and works in conjunction with the Raspberry Pi 4B to capture high-definition images that allow the system to recognize and classify objects.
The four-channel relay module contains four independent relay channels for controlling different output signals. During object recognition, depending on the material of the object (plastic, glass, metal or other), the Relay Module receives signals from the Raspberry Pi 4B and activates the corresponding output channels to send signals to the PLC. The Raspberry Pi 4B also sends the results of the system's detection and the number of individual materials to the Blynk IoT platform via Wi-Fi.

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