How to use ESP32 CAM with EDGE IMPULSE For OBJECT DETECTION | circuiTician

Summary

In this video, Shmabroto introduces a workspace safety monitoring device designed for construction areas, which uses the ESP32 camera and AI to detect whether workers are wearing safety helmets. He discusses the process of building this device, including data collection, annotation, model training, and deployment. The AI model, created using Edge Impulse, enables the device to make real-time decisions without relying on cloud connectivity. Shmabroto also shares his journey, his passion for electronics, and the milestone of his channel's fifth anniversary.

Takeaways

• 😀 Shmabroto returns to his YouTube channel Circian after a year-long break, apologizing for his absence due to business commitments and his passion for helping people bring electronics and robotics ideas to life.
• 😀 Shmabroto celebrates the fifth anniversary of Circian, thanking his supporters for five years of growth and pledging to create exciting content moving forward.
• 😀 Shmabroto introduces a new project: a workspace safety monitoring device designed for construction areas to ensure workers are wearing safety helmets.
• 😀 The project uses the ESP32 cam for vision and Age Impulse for on-device intelligence, combining AI for a practical safety application.
• 😀 The tutorial begins with data acquisition using the ESP32 cam to collect images of workers with and without helmets, which are labeled as 'safe' and 'unsafe' for the AI model.
• 😀 Data is annotated using Age Impulse's platform, where images are labeled by drawing bounding boxes around the regions of interest to help the AI model recognize safe and unsafe conditions.
• 😀 Age Impulse's object detection model is trained with labeled images, achieving 100% accuracy in detecting workers with and without helmets.
• 😀 Once the model is trained, it's deployed on the ESP32 cam using an Arduino library, allowing the device to run the model locally without needing a constant internet connection.
• 😀 A modification is made to the project where an LED flashlight turns on when an unsafe condition is detected, signaling when a worker is not wearing a helmet.
• 😀 The final demonstration shows the system working in real-time: when a worker wears a helmet, the system recognizes it as 'safe' and when the helmet is removed, it triggers the LED to indicate 'unsafe'.

Q & A

What is the main focus of the video content?

The main focus of the video is to demonstrate how to build a workspace safety monitoring device using an ESP32 camera and Edge Impulse for AI-based decision-making. The device is designed to monitor if construction workers are wearing safety helmets.

Why was Shmabroto away from making videos for a year?

Shmabroto was away from making videos because his business required a significant amount of his time and energy.

What role does Edge Impulse play in the project?

Edge Impulse is used to create the AI model that is deployed on the ESP32 camera. It enables the device to process images locally and make real-time decisions on whether a worker is wearing a helmet or not, without needing constant internet connectivity.

What hardware is used in this project?

The primary hardware used in this project is the ESP32 camera, which is a low-cost, powerful microcontroller with a built-in camera that can be used for image processing and AI inference.

What are the two categories of images captured during data acquisition?

The two categories of images captured during data acquisition are 'safe' images, where workers are wearing safety helmets, and 'unsafe' images, where workers are not wearing safety helmets.

How does the AI model process the images captured by the ESP32 camera?

The AI model processes the captured images by identifying whether a worker is wearing a safety helmet. It uses object detection techniques, where bounding boxes are drawn around the region of interest (e.g., the worker's head and helmet), and labels the image as either 'safe' or 'not safe' based on the presence of the helmet.

What is the role of the bounding boxes in the image annotation process?

Bounding boxes are drawn around the region of interest, such as the worker's head and helmet, to help the AI model recognize the objects it needs to focus on. Properly annotated images with bounding boxes improve the model's ability to make accurate predictions.

How does the model detect unsafe conditions in real-time?

Once the AI model is deployed on the ESP32 camera, it uses local processing to detect if a worker is not wearing a helmet. If the model identifies an unsafe condition, the system triggers an LED flashlight as an alert, indicating that safety is compromised.

What was the achievement of Shmabroto's university team in the ISRO Robotics Challenge?

Shmabroto mentored a team of university students who participated in the ISRO Robotics Challenge 2024 and achieved an impressive fifth place.

What additional feature did Shmabroto add to the ESP32 camera project?

Shmabroto added a feature where the LED flashlight will glow when an unsafe condition is detected (i.e., when a worker is not wearing a helmet). This serves as a visual alert to indicate that safety protocols are not being followed.