Unstructured Data Processing with a Raspberry Pi AI Kit - Hailo Edge AI Demo

Unstructured Data Engineering for AI
19 Jun 202407:17

TLDRThe video demonstrates an AI kit extension for the Raspberry Pi 5, showcasing real-time object detection using a camera. It integrates with a Slackbot, uploads images to MinIO, and performs image embedding. The AI kit's processing capabilities are highlighted, along with its ability to detect and categorize various objects with confidence scores. The setup includes a live Slack channel for notifications and uses open-source code, running efficiently on a portable device.

Takeaways

  • 😀 The video demonstrates extending the Raspberry Pi 5 AI kit with Halo example code for object detection.
  • 🔍 The program connects to a Milis and a Slackbot to process and communicate detection results.
  • 📸 It captures images and stores them with metadata such as detection label, file name, S3 path, and confidence level.
  • 🕒 The AI kit processes images in real-time, with the ability to sample and save files at intervals.
  • 📡 The system is designed to detect objects and post notifications to Slack with the label and confidence of the detection.
  • 📁 Images are uploaded to Mino with a file name that includes the label and a unique identifier (UID).
  • 🔗 The script includes functionality for image embedding and sending additional metadata fields.
  • 🔄 The program loops continuously, with standard code handling the main operations.
  • 📹 The AI kit is capable of live detection using a regular camera, showcasing the device's AI accelerator.
  • 🔍 The video includes a live demo where the AI kit detects various objects, such as a keyboard, a computer, and a person.
  • 🔎 The script also features a search functionality to find and display images based on labels with their metadata.

Q & A

  • What is the purpose of the program described in the transcript?

    -The program extends the Raspberry Pi 5 AI kit with Halo example code to perform object detection, processing images in real-time and sending alerts and data to various platforms such as Slack and MinIO.

  • How does the program integrate with a camera?

    -The program connects to a camera and uses the Raspberry Pi's AI accelerator to perform live detection of objects within the camera's field of view.

  • What is the role of the Slackbot in this setup?

    -The Slackbot is used to receive notifications about detected objects, including the label, confidence level, and a timestamp, which are posted to a Slack channel.

  • How does the program handle storing images and their metadata?

    -The program saves detected images locally with filenames based on the label and a unique identifier (uid). It also uploads these images to MinIO, along with their metadata.

  • What is the significance of the 'milis' mentioned in the transcript?

    -Milis likely refers to a MinIO instance, which is used for storing and managing the images and their associated metadata.

  • How does the program determine the confidence of its detections?

    -The program calculates the confidence level of its detections based on the AI model's output, indicating how certain the model is about the identified object.

  • What is the function of the 'feature extractor' mentioned in the script?

    -The feature extractor is a component that processes the images to extract relevant features that are then used for object detection and classification.

  • How frequently does the program sample and process images?

    -The program takes samples periodically, but the exact frequency is not specified in the transcript. It processes images when an object is detected, which can happen in real-time depending on the system's capabilities.

  • What is the process for searching and retrieving images based on their embeddings?

    -The program uses the embeddings of the images, which are generated from the feature extractor, to search and retrieve similar images from the MinIO storage.

  • How does the program handle real-time updates to the Slack channel?

    -The program posts real-time updates to the Slack channel whenever an object is detected, including details like the label, confidence, and a link to the image.

  • What is the potential use case for this program?

    -The program can be used for various applications such as surveillance, object tracking, or content moderation, where real-time detection and notification of specific objects are required.

Outlines

00:00

🤖 Raspberry Pi 5 AI Kit Enhancement

The speaker is experimenting with a program that extends the Raspberry Pi 5 AI kit's Halo example code for object detection. They have added custom features to connect to a Slackbot and a feature extractor. The program logs detected objects with details such as file name, S3 storage path, confidence level, and an actual vector. The speaker also mentions standard code and the process of capturing and saving images with labels and unique identifiers (UIDs). They discuss the real-time detection and uploading process to MinIO and Slack, including the challenges of managing multiple tasks simultaneously. The speaker concludes by demonstrating the program's ability to detect various objects and upload them to MinIO, with the detection results also appearing in a Slack channel.

05:04

🔍 Real-Time Object Detection and Search

In this segment, the speaker demonstrates the real-time object detection capabilities of their system, which is connected to a camera and utilizes AI for live detection. They show how the system can segment images and perform searches using metadata. The speaker also mentions the use of Boto3 for S3 integration and the speed of the search functionality. They proceed to search for specific objects like keyboards and TVs, showcasing the system's ability to retrieve and display the latest images with their metadata. The speaker emphasizes that all the code is open source and runs on a Raspberry Pi, highlighting the portability and efficiency of the system. They conclude by inviting viewers to a Meetup to see the system in action and thank them for their attention.

Mindmap

Keywords

💡Raspberry Pi

The Raspberry Pi is a series of small single-board computers developed in the UK by the Raspberry Pi Foundation. In the context of the video, it is used as the hardware platform for running AI applications. The script mentions extending the Raspberry Pi 5 AI kit, which implies using it for AI-related tasks such as object detection and image processing.

💡AI Kit

An AI Kit typically refers to a set of tools, software, and possibly hardware designed to facilitate the development of artificial intelligence applications. The video script discusses extending the capabilities of the Raspberry Pi 5 AI kit, suggesting that it includes pre-existing code and tools for AI tasks, which the presenter is enhancing with their own code.

💡Object Detection

Object detection is a computer vision technique that involves identifying and locating objects in an image or video. The script describes the program's ability to detect objects, which is a core part of the AI demo being showcased. The AI kit is used to process images from a camera in real-time to detect and identify various objects.

💡Feature Extractor

A feature extractor in machine learning is a component that identifies and encodes the most important features of input data for use in model training or inference. In the script, the feature extractor is mentioned as part of the process where the AI identifies objects in the images, suggesting that it plays a role in the object detection process.

💡Slackbot

A Slackbot is a custom bot designed to interact with users within the Slack platform, often used for automation and communication tasks. The video script mentions connecting to a Slackbot, indicating that the AI system is integrated with Slack to send notifications or updates about the detection process, such as the label and confidence of detected objects.

💡Mili

Mili (or Milvus) is an open-source vector database designed for embedding similarity search and AI applications. The script refers to 'Mili' as a storage solution for the detected objects' data, including the vector representation of the images, which is crucial for performing similarity searches or further analysis.

💡Vector

In the context of AI and machine learning, a vector is a mathematical representation of the features of an object, often used in tasks like image recognition. The video script mentions creating and storing vectors for detected objects, which are then used for searching and identifying similar items in the database.

💡S3 Path

S3 Path refers to the location within Amazon S3, which is a scalable storage service offered by Amazon Web Services. In the script, the S3 path is mentioned as the storage location for the images that the AI system processes, indicating that the system is designed to work with cloud storage solutions.

💡Confidence

In machine learning, confidence refers to the probability or certainty of a model's prediction. The script discusses the AI system providing a confidence score for each detected object, which helps in assessing the reliability of the detection.

💡Image Embedding

Image embedding is the process of converting an image into a numerical representation (vector) that captures its essential features. The script mentions sending image embeddings with metadata, which is used for searching similar images in the database, highlighting the importance of this process in the AI system's functionality.

💡Metadata

Metadata is data that provides information about other data. In the context of the video, metadata includes details such as the label of the detected object, the confidence score, the file name, and the S3 path. This information is crucial for organizing and searching the data stored in the system.

Highlights

Extending the Raspberry Pi 5 AI kit with Halo example code for detection.

Connecting to Milis and Slackbot for feature extraction and notifications.

Storing detected objects with metadata including label, file name, S3 path, confidence, and vector.

Utilizing the AI kit's processing power to handle real-time detection.

Sampling detection at intervals to manage processing load.

Saving detected files locally with labels and unique identifiers.

Updating Mino with detection results and posting to Slack.

Uploading camera images to Slack for real-time updates.

Performing image embedding and sending metadata for storage.

Looping through standard code for continuous operation.

Detecting various objects like a keyboard, computer, and potentially a duck or cat.

Real-time detection and Slack notifications are demonstrated.

Uploading detected images and metadata to Mino.

Using Mino's object browser to view stored data.

Filtering and searching for specific labels in the stored data.

Using a notebook with Boto3 for S3 integration.

Searching for specific objects using embeddings and metadata.

Downloading and displaying search results with metadata.

All code is open source and runs on a Raspberry Pi with an AI accelerator.

The system includes image segmentation and other advanced features.

The demo is portable and could be brought to events for live demonstrations.

Create Stunning AI Art Instantly

No login. No cost. Just incredible AI images !

Create Stunning AI Art InstantlyGenerate Now