AI That Talks for the Blind | Real-Time Object Detection with Voice Assistance using RDK X5

Last Updated on March 11, 2026 by Engr. Shahzada Fahad

Table of Contents

Why Traditional Ultrasonic Sensors Fail (And How AI for the Visually Impaired Fixes It)

AI That Talks for the Blind | Real-Time Object Detection with Voice Assistance- If you cannot see, is it really enough to only know that something is in front of you? To achieve true independence, a real-time vision system must provide more than just proximity alerts; it must identify the world.

Most YouTube videos and articles about assistive technology for the blind rely on simple ultrasonic sensors. These sensors do only one thing: they detect an obstacle and give a beep. While this is helpful for basic obstacle recognition, it is not enough for complex, real-world navigation.

A visually impaired person should know what is in front of them, not just that something exists.

Is it a wall?

Is it a car?

Is it a human standing nearby?

This difference matters. Life is not only about avoiding obstacles. It is about understanding the environment and feeling confident while moving. That is why I decided to build AI that talks for the blind using advanced computer vision for accessibility

Instead of a simple beep sound, this system uses AI vision for blind people to recognize objects and speak their names clearly. Imagine hearing:

“Human in front”

“Bike ahead”

“Door on the right”

This kind of voice-guided navigation can make everyday movement much safer and more independent.

There is another serious challenge that often gets ignored; money identification. Visually impaired people cannot easily identify currency notes. They are forced to trust others, which can lead to mistakes. So I designed an AI object recognition system that can see objects and speak their names. It can also be extended to recognize currency notes and custom objects.

This should not happen.

So I designed an AI object recognition system that can see objects and speak their names. It can also be extended to recognize currency notes and even custom objects that are not included in standard COCO datasets.

This smart assistive device for blind people is not just a project; it is a step toward real independence.

In this article, I will show you how this system works, how it detects objects, how it speaks, and how it can help visually impaired people in real life.

Amazon Links:

RDK X5 Development Board

Other Tools and Components:

ESP32 WiFi + Bluetooth Module (Recommended)

Arduino Nano USB C type (Recommended)

*Please Note: These are affiliate links. I may make a commission if you buy the components through these links. I would appreciate your support in this way!

For this project, I am using the RDK X5 by D-Robotics along with a camera.

You can use either a MIPI camera or a USB camera. In my previous article, I explained both MIPI and USB cameras in detail. After reading that article, you will clearly understand how to use both types of cameras.

So; for this project, I can use either of the two cameras. But after thinking carefully, I decided to use a USB camera because of its flexibility as a wearable AI camera setup.

In a project for visually impaired people, I don’t know which camera someone may prefer to use. USB cameras come in many different designs and sizes, and they are easy to find. Because of this flexibility, I felt that using a USB camera would be a better choice. This way, more people can easily follow and build this project using the camera that suits them best.

You will also need a headphone. On the RDK X5, you get a 3.5 mm headphone jack for audio input and output.

Because of this, you do not need any external amplifier board. You can connect any standard headphone directly to the RDK X5 for your voice-guided assistant.

During the practical demo, I want you to hear everything clearly. That is why I am using these larger speakers.

The interesting part is that these speakers do not need an external power supply. I can power them directly using any USB port on the RDK X5.

Even though this is a complex project, it feels very easy on the RDK X5. If we try to build the same project using another board, we would need many extra boards. Even if we use the latest Raspberry Pi, we would still need an AI HAT; the RDK-X5 object detection capabilities are already built-in.

That is why I really like the RDK X5. Everything is very user-friendly. There is no need for an AI HAT, and the AI capabilities are already built in. I have already made a full dedicated video about the AI features of the RDK X5. After watching that video, you will clearly see how powerful this board really is.

To make this whole system portable, I am using my own designed 5-volt 3-amp power supply, along with a 4S lithium-ion battery that I built myself.

As you can see, I have powered up the complete system and I am accessing the RDK X5 remotely from my laptop using VNC.

Now let’s give the RDK X5 a voice. For that, we first need to install eSpeak, a powerful Python text-to-speech engine. Don’t worry, this part is very simple. While you are on the desktop, right-click the mouse and select Open Terminal Here…

Once the terminal opens, just run these commands, and in a moment, your RDK X5 will be ready to speak.

sudo apt update
sudo apt install espeak

1 2	sudo apt update sudo apt install espeak

Testing Text-to-Speech Output on RDK X5

I wrote a very simple Python script just to test the voice-guided alerts on the RDK X5. The main goal is to make sure that eSpeak is working properly and that the audio is clear. The script sends a few text messages to the speaker with small pauses in between, so we can clearly hear each sentence. This helps me confirm that the system is ready before moving to more advanced features.

Text-to-Speech Test Code for RDK X5

#!/usr/bin/env python3
import subprocess
import time

def speak(text):
    print(f"System saying: '{text}'")
    try:
        # -s 140 sets the speed (words per minute). Default is often too fast/robotic.
        # -v en-us sets the voice to US English (optional)
        subprocess.run(['espeak', '-s', '140', text], check=True)
    except FileNotFoundError:
        print("ERROR: 'espeak' is not installed.")
        print("Please install it using: sudo apt-get install espeak")
    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    print("--- Audio Test Started ---")
    
    speak("System initialization complete.")
    time.sleep(0.5)
    
    speak("Testing left speaker.")
    time.sleep(0.5)
    
    speak("Testing right speaker.")
    time.sleep(0.5)
    
    speak("Welcome to Electronic Clinic")
    
    print("--- Audio Test Finished ---")

#!/usr/bin/env python3

import subprocess

import time

def speak(text):

print(f"System saying: '{text}'")

try:

# -s 140 sets the speed (words per minute). Default is often too fast/robotic.

# -v en-us sets the voice to US English (optional)

subprocess.run(['espeak', '-s', '140', text], check=True)

except FileNotFoundError:

print("ERROR: 'espeak' is not installed.")

print("Please install it using: sudo apt-get install espeak")

except Exception as e:

print(f"An error occurred: {e}")

if __name__ == "__main__":

print("--- Audio Test Started ---")

speak("System initialization complete.")

time.sleep(0.5)

speak("Testing left speaker.")

time.sleep(0.5)

speak("Testing right speaker.")

time.sleep(0.5)

speak("Welcome to Electronic Clinic")

print("--- Audio Test Finished ---")

Now, let’s run this code.

Text-to-Speech Practical Demo:

Note: for the practical demo, watch the video tutorial on my YouTube Channel “Electronic Clinic”. The Video link is given at the end of this article.

As you just heard, the system is speaking clearly. The voice does sound a little robotic, but it is easy to understand. For this assistive technology project, clarity is the priority, not a human-like voice. What matters most is that the RDK X5 can speak reliably and deliver information without confusion.

Later, we can always improve the voice quality using more advanced speech engines. But for now, this confirms that the speech system is working correctly and is ready to be used for object recognition and other important alerts.

Building the AI for the Visually Impaired: Python Code & Logic

Now this is the final code for the system, which uses YOLO model deployment logic. I will not go into every single line, because the core idea is very simple. The camera continuously looks at what is in front of the user, the AI model detects objects, and then the system decides which objects actually matter.

Right now, I am only focusing on a selected list of important objects, like people, vehicles, traffic signs, and animals. These include objects such as a person, bicycle, car, motorcycle, bus, train, truck, traffic light, stop sign, fire hydrant, cat, and dog. The system only reacts to objects that are directly in front and close enough, so the user does not get overloaded with unnecessary information.

When one of these objects is detected in a critical position, the system speaks its name using audio. So instead of hearing random beeps, the visually impaired person gets voice-guided navigation with clear and meaningful information, like “person ahead” or “car in front,” which is far more useful in real life.

The good thing is that this object list is not fixed. You can easily add more object names to this list based on your requirements. And if the default objects are not enough, you can also train your own custom AI models and use them with this system. That means you can teach it to recognize specific objects, environments, or even local use-case scenarios.

The best part is that everything runs directly on the RDK X5. No extra AI hardware is needed, and the system works in real time.This makes the setup a truly practical electronic travel aid.

Download the AI for the Visually Impaired Source Code

Download the entire project folder from my Patreon Page.

Now let’s run this code. Make sure you are inside the same folder. Right-click the mouse and select Open Terminal Here. After that, type this command.

sudo python3 blind.py

1	sudo python3 blind.py

Real-World Testing: How the AI for the Visually Impaired Performs

On the screen, you can see two blue lines. Any object that appears between these two lines means it is directly in front of the visually impaired person. If an object is detected on the left or right side, the system simply ignores it. This is done on purpose, so the user does not get disturbed by unnecessary voice alerts.

I have also taken special care of distance. If a person or any object is far away from the user, there will be no voice alert.

As you can see right now, I am standing in front of the camera, but there is no voice alert because my distance is still too much. This distance threshold is fully adjustable, and you can set it according to your own requirement.

Now you can see that I am much closer to the camera, but still there is no voice alert. That is because I am not exactly in front of the camera.

In a real setup, this camera will be mounted on the chest or head of the visually impaired person. Right now, the camera is placed on the desk, so I need to move myself directly in front of it.

As you can see, the moment I come right in front of the wearable AI camera, the voice alert is generated. The gap between each voice alert is also adjustable. Everything can be controlled from the code according to your preference. As long as I stay in front of the camera, the system will keep generating voice alerts.

If I move to the side, the voice alerts stop immediately.

As long as an object is far away, there will be no voice alert. But as soon as any person or object starts coming closer, the voice alert starts.

After hearing this, the visually impaired person can turn left or right and safely adjust their path.

Now I am going to test this system with this bike.

This bike is smaller than a real one, so it needs to come much closer to the camera before a voice alert is generated.

Earlier, the camera was stationary and placed on the desk. That was just to clearly show how the detection logic works.

Now, I have mounted the camera on my chest.

This is much closer to a real-world FPV AI vision scenario. I am doing this to demonstrate that the system works even when the user is moving.

As you can see, even in motion, the system is still able to detect humans and objects in real-time AI detection..

The camera movement does not confuse the AI. It continuously analyzes what is in front and gives voice alerts only when an important object comes directly in front and close enough.

This is very important, because visually impaired people are always in motion. They are walking, turning, and adjusting their direction. A system like this must work reliably while moving, not just when standing still.

This test shows that the RDK X5 can easily handle computer vision for accessibility and audio feedback together, even in a wearable setup. That makes this system practical, portable, and ready for real-world use.

So, that’s all for now.