[Tutorial] : Automated Quality Inspection System Using AI & Favoriot

Step-by-Step Guide: Automated Quality Inspection System Using AI & Favoriot

This guide outlines how to develop an Automated Quality Inspection System using AI, a camera, and the Favoriot IoT platform. The provided code is in Python, along with detailed explanations.

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Step 1: Hardware & Software Preparation

Before getting started, ensure you have:

• Raspberry Pi (or any computer with a camera)
• HD Camera (Raspberry Pi Camera or USB Camera)
• Python Installed
• OpenCV & TensorFlow Libraries for image analysis
• Internet Connection to send data to Favoriot

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Step 2: Install Required Software

Run the following commands to install the necessary libraries:

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip -y
pip3 install opencv-python numpy requests tensorflow


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Step 3: Train an AI Model for Defect Detection

You can use Transfer Learning with a pre-trained model like MobileNetV2. Below is an example of training the model:

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Dataset configuration
dataset_path = "dataset_defects"
batch_size = 32
img_size = (224, 224)

datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_data = datagen.flow_from_directory(
    dataset_path,
    target_size=img_size,
    batch_size=batch_size,
    class_mode="binary",
    subset="training"
)

val_data = datagen.flow_from_directory(
    dataset_path,
    target_size=img_size,
    batch_size=batch_size,
    class_mode="binary",
    subset="validation"
)

# Transfer Learning Model
base_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights="imagenet")
base_model.trainable = False

model = tf.keras.Sequential([
    base_model,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(1, activation="sigmoid")
])

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])

# Train the model
model.fit(train_data, validation_data=val_data, epochs=10)

# Save the model
model.save("defect_detection_model.h5")


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Step 4: Real-Time Defect Detection with Camera

This code captures images from the camera and detects defects using the trained AI model.

import cv2
import numpy as np
import tensorflow as tf

# Load trained model
model = tf.keras.models.load_model("defect_detection_model.h5")

# Start camera
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Preprocess image
    img = cv2.resize(frame, (224, 224))
    img = np.expand_dims(img, axis=0) / 255.0

    # Predict defects
    prediction = model.predict(img)[0][0]

    # Display result on screen
    label = "Defect Detected!" if prediction > 0.5 else "Product OK"
    color = (0, 0, 255) if prediction > 0.5 else (0, 255, 0)

    cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
    cv2.imshow("Quality Inspection", frame)

    # Exit when 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()


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Step 5: Send Defect Reports to Favoriot

The system automatically sends the data to the Favoriot IoT Platform if a defect is detected.

1. Create a Device on Favoriot

• Log in to Favoriot.
• Navigate to Devices → Add Device.
• Copy the Device ID and API Key.

2. Code to Send Data to Favoriot

import requests
import json

# Favoriot credentials
DEVICE_ID = "YOUR_DEVICE_ID"
API_KEY = "YOUR_FAVORIOT_API_KEY"
URL = "https://apiv2.favoriot.com/v2/streams"

def send_data_to_favoriot(status):
    payload = {
        "device_developer_id": DEVICE_ID,
        "data": {
            "status": status
        }
    }
    
    headers = {
        "Content-Type": "application/json",
        "Apikey": API_KEY
    }
    
    response = requests.post(URL, data=json.dumps(payload), headers=headers)
    print("Response from Favoriot:", response.json())

# Example usage (send data if defect detected)
if prediction > 0.5:
    send_data_to_favoriot("Defect Detected!")
else:
    send_data_to_favoriot("Product OK")


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Step 6: Monitor Data on Favoriot

• Go to Favoriot Dashboard → Streams to view defect reports.
• You can also create a dashboard to visualize real-time defect detection data.

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Conclusion

With this system, you can:

✅ Automatically detect defects using AI.
✅ Display results in real-time on the screen.
✅ Send defect reports to Favoriot for remote monitoring.

This system can be enhanced by adding Telegram notifications or advanced analytics on Favoriot.

[Disclaimer: This article provides a step-by-step guide. The source code may need adjustments to fit the final project design.]