Exploring the most important Object Detection models and tools applied to the edge
I built a project to identify birds at a bird feeder using Machine Learning (TinyML) and transmit data to the cloud with a Blues Notecard
Dual AI Camera using Grove Vision AI V2 and Xiao ESP32S3 Sense to detect and capture images of hummingbirds.
Deploying YOLOv8 on Raspberry Pi Zero 2W for Real-Time Bee Counting at the Hive Entrance.
Build an object detection model on Edge Impulse that displays both available and unavailable parking spots.
Monitor road usage with this camera-equipped device, which detects and logs vehicle traffic to a cloud database.
Exploring Computer Vision applications such as Image Classification, Object Detection, and Pose estimation.
Learn how to deploy NVIDIA TAO Object Detection ML models on the NXP i. MX 93's dual-core Arm Cortex-A55 CPU and Ethos-U65 NPU.
Computer Vision Exploration with the help of Edge Impulse and Arduino.
Computer Vision Exploration with the help of OpenMV, Edge Impulse, and Arduino.
A demo on how to implement object detection and tracking found objects. Edge Impulse Linux Python SDK is used for the object detection
Star Wars Pit Droid powered by NVIDIA Jetson Orin Nano and vision AI.
Cargus monitors the baby car seat. If it detects a baby in the car seat and an idle car, it will send a text message to the parent.
Towards Spatial-guided Vision based Assistive Robotics using EdgeImpulse and LEGO Mindstorms.
Driver ID, location/velocity data, seatbelt, aggressive driving and alcohol detection for Fatal Accident Risk Mitigation (FARM) in vehicles.
Detecting short circuits, open circuits and missing holes in PCB boards with Object Detection and deploying to a Raspberry Pi
This project is an example of how AI can be used for counting objects in a quick and efficient way using embedded Machine Learning.
Learn how to easily set up machine learning inference capabilities on the AMD/Xilinx KV260 Vision AI starter kit running Ubuntu desktop
With thermal imaging, glass acts as a mirror. Therefore, you can use picture frames and other glass objects to see reflections!
When Xilinx launched Kria™, they also provided a method for quickly deploying apps. This project takes a look at creating a custom app.
Deploying and inferencing a TFLite model trained using Edge Impulse Studio on M5Stack UnitV2
Here, I will be implementing a machine learning model on a remote Raspberry Pi connected over a cellular module.
The ML Yocto image includes an eIQ layer so you can run machine learning inference efficiently on the MaaXBoard.
Get started using a Pi 3 with Intel's Neural Compute Stick 2, to detect objects and faces.
