An intelligent conveyor belt system that uses computer vision, sensors, and embedded control to detect, classify, measure, and sort objects automatically.

This project integrates: • Raspberry Pi (vision processing + system logic) • Raspberry Pi Pico (real-time hardware control) • YOLO object detection • Infrared break-beam sensors • Ultrasonic sensors • Servo-based diverter • Relay-controlled conveyor motor

The system detects objects on a conveyor belt, measures their physical dimensions, analyzes them using AI, and then routes them to the correct destination.

Camera │ ▼ Raspberry Pi (AI + Logic) │ │ USB Serial ▼ Raspberry Pi Pico (Real-time Control) │ ├── Relay → Conveyor Belt ├── Servo → Diverter Gate ├── IR Break Beam Sensors └── Ultrasonic Sensors

The Raspberry Pi handles AI and decision-making, while the Pico manages real-time hardware control.

Processing • Raspberry Pi • Raspberry Pi Pico

Sensors • 2 × IR Break-Beam Sensors (object detection + timing) • 2 × Ultrasonic Sensors (dimension measurement)

Actuators • SC90 Micro Servo (sorting gate) • Relay Module (conveyor motor control)

Vision • USB Camera

Conveyor • 24V Stainless Steel Conveyor Belt • Speed: ~50 mm/s

Raspberry Pi

Physical Pin GPIO Function Pin 2 5V Power Pin 6 GND Ground Pin 12 GPIO18 Servo signal Pin 13 GPIO17 Ultrasonic 1 TRIG Pin 15 GPIO22 Ultrasonic 1 ECHO Pin 16 GPIO23 Ultrasonic 2 TRIG Pin 18 GPIO24 Ultrasonic 2 ECHO Pin 22 GPIO25 IR Sensor 1 Pin 29 GPIO5 IR Sensor 2

Object length is calculated using beam interruption timing:

Timer1 = time IR1 beam is broken Timer2 = time conveyor is stopped

Object Length = (Timer1 - Timer2) × Belt Speed

Example:

Beam broken duration: 8 seconds Conveyor stopped: 2 seconds Movement time: 6 seconds

Length = 6 × 50 mm/s = 300 mm

After analysis, the object is routed via a servo gate.

User-defined rules determine routing.

Example rule file:

Object: apple Color: red Width: 50 Length: 80 Thickness: 20 Min_conf: 0.4 Path: C5

The system interprets the path and directs the object accordingly.

The system uses YOLO (Ultralytics) for object detection.

Each object is analyzed over 600 frames to obtain: • object classification • average confidence • color estimation • detection stability

Model used:

YOLOv11 Nano

The Raspberry Pi communicates with the Pico via USB serial.

Commands:

RUN STOP PING

Example response:

OK RUN OK STOP PONG

MIT License