This guide will show you how to build an AI-powered quality inspection system using a camera and send inspection results to the FAVORIOT IoT platform in real time.

🔧 Step 1: What You Need

Hardware:

• Raspberry Pi (or any computer with a camera)
• Camera (USB or Pi Camera)
• Internet connection

Software:

• Python 3
• Libraries: opencv-python, tensorflow, numpy, requests

🛠️ Step 2: Install the Required Software

Open Terminal and run:

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

🧠 Step 3: Train an AI Model to Detect Defects

Create a folder called dataset_defects with 2 subfolders: defect and normal.

Now, use this Python code to train the model:

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

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"
)

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"])
model.fit(train_data, validation_data=val_data, epochs=10)

model.save("defect_detection_model.h5")

🎥 Step 4: Real-Time Defect Detection Using Camera

Once the model is trained and saved, run this script:

import cv2
import numpy as np
import tensorflow as tf

model = tf.keras.models.load_model("defect_detection_model.h5")
cap = cv2.VideoCapture(0)

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

    img = cv2.resize(frame, (224, 224))
    img = np.expand_dims(img, axis=0) / 255.0
    prediction = model.predict(img)[0][0]

    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)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

🌐 Step 5: Send Defect Results to FAVORIOT

✅ 1. Set Up a Device in FAVORIOT

1. Log in to Favoriot Platform
2. Go to Devices → Add Device
3. Note down your Device Developer ID and API Key

✅ 2. Add Code to Send Data

Below is the function to send results:

import requests
import json

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())

✅ 3. Combine with Real-Time Detection

Add this snippet inside your prediction logic:

if prediction > 0.5:
    send_data_to_favoriot("Defect Detected!")
else:
    send_data_to_favoriot("Product OK")

📊 Step 6: View Data on FAVORIOT Dashboard

• Go to your device on the FAVORIOT Dashboard
• Click on Streams to view defect data
• You can also create graphs or alert rules for monitoring

🚀 Bonus Tips

• Add Telegram Alerts using Telegram Bot API
• Add Dashboard Charts using Favoriot’s visualization
• Improve accuracy with better dataset or model tuning

✅ Summary

With this project, you have:

✅ Built a real-time defect detection system
✅ Displayed results on screen
✅ Sent reports to FAVORIOT cloud platform

References

• Favoriot Platform Registration (FREE and Paid)
• Favoriot Full Documentation (Official)
• Favoriot Github
• How-To Use Favoriot Platform Video Playlist

Disclaimer

This article provides a step-by-step guide and only serves as a guideline. The source code may need adjustments to fit the final project design.

Favoriot Edge Gateway is a service that connects IoT gateways directly to the Favoriot Platform. It bridges the “edge” (local devices) and the Favoriot Platform, enabling data from local devices to be processed, managed, and stored. Additionally, it allows for the selection of specific parameters in the payload to be stored, providing greater control over transmitted data.

Key References

• Official documentation: https://platform.favoriot.com/tutoria…
• Favoriot Platform: https://platform.favoriot.com
• Favoriot Website: https://www.favoriot.com/

🚀 Stay Ahead with Seamless Firmware Updates!

In the fast-paced world of IoT, keeping your devices updated is crucial for security, performance, and functionality. But imagine having to manually update thousands of devices deployed across different locations—tedious, costly, and highly inefficient, right?

That’s where Firmware Over-the-Air (OTA) updates come in, and in this video, we’ll show you how the Favoriot Platform makes firmware updates effortless!

Why do Firmware OTA Updates Matter?

1️⃣ Security First 🔒 – Cyber threats evolve rapidly, and outdated firmware is a prime target. OTA updates allow you to patch vulnerabilities quickly, ensuring your IoT network remains secure.

2️⃣ Cost & Time Efficiency ⏳💰 – No need for on-site visits or manual intervention. Update thousands of devices remotely with a single click, reducing operational costs.

3️⃣ Feature Enhancements 🚀 – Want to roll out new features? OTA updates let you upgrade device functionalities without recalling hardware, keeping your devices innovative and competitive.

4️⃣ Bug Fixes & Performance Optimization 🛠️ – Ensure smooth performance by addressing software glitches before they impact users. A quick OTA update can resolve issues instantly.

5️⃣ Scalability & Future-Proofing 📡 – As your IoT ecosystem grows, maintaining a fleet of devices becomes challenging. OTA ensures easy scalability, allowing remote updates as your network expands.

Introducing Favoriot OTA

The Favoriot platform supports OTA (Over-the-Air) capabilities for IoT devices, enabling seamless remote updates of firmware, software, and configurations. This ensures devices remain secure, functional, and up-to-date without physical intervention. Key features include:

1. Firmware and Software Updates: Delivering updates to fix bugs, enhance features, or patch vulnerabilities.
2. Configuration Management: Pushing new settings to devices remotely.
3. Security: Quickly deploying patches to address vulnerabilities.
4. Scalability: Managing updates across large fleets of IoT devices efficiently.

Favoriot’s firmware OTA functionality is essential for maintaining the performance, security, and longevity of IoT deployments in applications such as smart homes, industrial IoT, and connected vehicles.

This powerful feature eliminates the hassle of manual updates, ensuring your IoT infrastructure stays secure, efficient, and up to date—no matter where your devices are deployed!

(NOTE: Favoriot Firmware OTA is only available in the Developer Plan)

🎥 Watch this video to see Favoriot OTA in action and learn how to update your IoT devices remotely!

Key References

• Official documentation: https://platform.favoriot.com/tutoria…
• Favoriot Github: https://github.com/Favoriot
• Favoriot Platform: https://platform.favoriot.com
• Favoriot Website: https://www.favoriot.com/