LogoLogo
HomeDocsAPI & SDKsProjectsForumStudio
  • Welcome
    • Featured Machine Learning Projects
      • Getting Started with the Edge Impulse Nvidia TAO Pipeline - Renesas EK-RA8D1
      • Smart City Traffic Analysis - NVIDIA TAO + Jetson Orin Nano
      • ROS 2 Pick and Place System - Arduino Braccio++ Robotic Arm and Luxonis OAK-D
      • Optimize a cloud-based Visual Anomaly Detection Model for Edge Deployments
      • Rooftop Ice Detection with Things Network Visualization - Nvidia Omniverse Replicator
      • Surgery Inventory Object Detection - Synthetic Data - Nvidia Omniverse Replicator
      • NVIDIA Omniverse - Synthetic Data Generation For Edge Impulse Projects
      • Community Guide – Using Edge Impulse with Nvidia DeepStream
      • Computer Vision Object Counting - Avnet RZBoard V2L
      • Gesture Appliances Control with Pose Detection - BrainChip AKD1000
      • Counting for Inspection and Quality Control - Nvidia Jetson Nano (TensorRT)
      • High-resolution, High-speed Object Counting - Nvidia Jetson Nano (TensorRT)
    • Prototype and Concept Projects
      • Renesas CK-RA6M5 Cloud Kit - Getting Started with Machine Learning
      • TI CC1352P Launchpad - Getting Started with Machine Learning
      • OpenMV Cam RT1062 - Getting Started with Machine Learning
      • Getting Started with Edge Impulse Experiments
  • Computer Vision Projects
    • Workplace Organizer - Nvidia Jetson Nano
    • Recyclable Materials Sorter - Nvidia Jetson Nano
    • Analog Meter Reading - Arduino Nicla Vision
    • Creating Synthetic Data with Nvidia Omniverse Replicator
    • SonicSight AR - Sound Classification with Feedback on an Augmented Reality Display
    • Traffic Monitoring - Brainchip Akida
    • Multi-camera Video Stream Inference - Brainchip Akida
    • Industrial Inspection Line - Brainchip Akida
    • X-Ray Classification and Analysis - Brainchip Akida
    • Inventory Stock Tracker - FOMO - BrainChip Akida
    • Container Counting - Arduino Nicla Vision
    • Smart Smoke Alarm - Arduino Nano 33
    • Shield Bot Autonomous Security Robot
    • Cyclist Blind Spot Detection - Himax WE-I Plus
    • IV Drip Fluid-Level Monitoring - Arduino Portenta H7
    • Worker PPE Safety Monitoring - Nvidia Jetson Nano
    • Delivered Package Detection - ESP-EYE
    • Bean Leaf Disease Classification - Sony Spresense
    • Oil Tank Measurement Using Computer Vision - Sony Spresense
    • Object Counting for Smart Industries - Raspberry Pi
    • Smart Cashier with FOMO - Raspberry Pi
    • PCB Defect Detection with Computer Vision - Raspberry Pi
    • Bicycle Counting - Sony Spresense
    • Counting Eggs with Computer Vision - OpenMV Cam H7
    • Elevator Passenger Counting - Arduino Nicla Vision
    • ESD Protection using Computer Vision - Seeed ReComputer
    • Solar Panel Defect Detection - Arduino Portenta H7
    • Label Defect Detection - Raspberry Pi
    • Dials and Knob Monitoring with Computer Vision - Raspberry Pi
    • Digital Character Recognition on Electric Meter System - OpenMV Cam H7
    • Corrosion Detection with Computer Vision - Seeed reTerminal
    • Inventory Management with Computer Vision - Raspberry Pi
    • Monitoring Retail Checkout Lines with Computer Vision - Renesas RZ/V2L
    • Counting Retail Inventory with Computer Vision - Renesas RZ/V2L
    • Pose Detection - Renesas RZ/V2L
    • Product Quality Inspection - Renesas RZ/V2L
    • Smart Grocery Cart Using Computer Vision - OpenMV Cam H7
    • Driver Drowsiness Detection With FOMO - Arduino Nicla Vision
    • Gastroscopic Image Processing - OpenMV Cam H7
    • Pharmaceutical Pill Quality Control and Defect Detection
    • Deter Shoplifting with Computer Vision - Texas Instruments TDA4VM
    • Smart Factory Prototype - Texas Instruments TDA4VM
    • Correct Posture Detection and Enforcement - Texas Instruments TDA4VM
    • Visual Anomaly Detection with FOMO-AD - Texas Instruments TDA4VM
    • Surface Crack Detection and Localization - Texas Instruments TDA4VM
    • Surface Crack Detection - Seeed reTerminal
    • Retail Image Classification - Nvidia Jetson Nano
    • SiLabs xG24 Plus Arducam - Sorting Objects with Computer Vision and Robotics - Part 1
    • SiLabs xG24 Plus Arducam - Sorting Objects with Computer Vision and Robotics - Part 2
    • Object Detection and Visualization - Seeed Grove Vision AI Module
    • Bike Rearview Radar - Raspberry Pi
    • Build a Self-Driving RC Vehicle - Arduino Portenta H7 and Computer Vision
    • "Bring Your Own Model" Image Classifier for Wound Identification
    • Acute Lymphoblastic Leukemia Classifier - Nvidia Jetson Nano
    • Hardhat Detection in Industrial Settings - Alif Ensemble E7
    • Motorcycle Helmet Identification and Traffic Light Control - Texas Instruments AM62A
    • Import a Pretrained Model with "Bring Your Own Model" - Texas Instruments AM62A
    • Product Inspection with Visual Anomaly Detection - FOMO-AD - Sony Spresense
    • Visual Anomaly Detection in Fabric using FOMO-AD - Raspberry Pi 5
    • Car Detection and Tracking System for Toll Plazas - Raspberry Pi AI Kit
    • Visual Anomaly Detection - Seeed Grove Vision AI Module V2
    • Object Counting with FOMO - OpenMV Cam RT1062
    • Visitor Heatmap with FOMO Object Detection - Jetson Orin Nano
    • Vehicle Security Camera - Arduino Portenta H7
  • Audio Projects
    • Occupancy Sensing - SiLabs xG24
    • Smart Appliance Control Using Voice Commands - Nordic Thingy:53
    • Glass Window Break Detection - Nordic Thingy:53
    • Illegal Logging Detection - Nordic Thingy:53
    • Illegal Logging Detection - Syntiant TinyML
    • Wearable Cough Sensor and Monitoring - Arduino Nano 33 BLE Sense
    • Collect Data for Keyword Spotting - Raspberry Pi Pico
    • Voice-Activated LED Strip - Raspberry Pi Pico
    • Snoring Detection on a Smart Phone
    • Gunshot Audio Classification - Arduino Nano 33 + Portenta H7
    • AI-Powered Patient Assistance - Arduino Nano 33 BLE Sense
    • Acoustic Pipe Leakage Detection - Arduino Portenta H7
    • Location Identification using Sound - Syntiant TinyML
    • Environmental Noise Classification - Nordic Thingy:53
    • Running Faucet Detection - Seeed XIAO Sense + Blues Cellular
    • Vandalism Detection via Audio Classification - Arduino Nano 33 BLE Sense
    • Predictive Maintenance Using Audio Classification - Arduino Nano 33 BLE Sense
    • Porting an Audio Project from the SiLabs Thunderboard Sense 2 to xG24
    • Environmental Audio Monitoring Wearable - Syntiant TinyML - Part 1
    • Environmental Audio Monitoring Wearable - Syntiant TinyML - Part 2
    • Keyword Spotting - Nordic Thingy:53
    • Detecting Worker Accidents with Audio Classification - Syntiant TinyML
    • Snoring Detection with Syntiant NDP120 Neural Decision Processor - Arduino Nicla Voice
    • Recognize Voice Commands with the Particle Photon 2
    • Voice Controlled Power Plug with Syntiant NDP120 (Nicla Voice)
    • Determining Compressor State with Audio Classification - Avnet RaSynBoard
    • Developing a Voice-Activated Product with Edge Impulse's Synthetic Data Pipeline
    • Enhancing Worker Safety using Synthetic Audio to Create a Dog Bark Classifier
  • Predictive Maintenance and Defect Detection Projects
    • Predictive Maintenance - Nordic Thingy:91
    • Brushless DC Motor Anomaly Detection
    • Industrial Compressor Predictive Maintenance - Nordic Thingy:53
    • Anticipate Power Outages with Machine Learning - Arduino Nano 33 BLE Sense
    • Faulty Lithium-Ion Cell Identification in Battery Packs - Seeed Wio Terminal
    • Weight Scale Predictive Maintenance - Arduino Nano 33 BLE Sense
    • Fluid Leak Detection With a Flowmeter and AI - Seeed Wio Terminal
    • Pipeline Clog Detection with a Flowmeter and AI - Seeed Wio Terminal
    • Refrigerator Predictive Maintenance - Arduino Nano 33 BLE Sense
    • Motor Pump Predictive Maintenance - Infineon PSoC 6 WiFi-BT Pioneer Kit + CN0549
    • BrickML Demo Project - 3D Printer Anomaly Detection
    • Condition Monitoring - Syntiant TinyML Board
    • Predictive Maintenance - Commercial Printer - Sony Spresense + CommonSense
    • Vibration Classification with BrainChip's Akida
    • AI-driven Audio and Thermal HVAC Monitoring - SeeedStudio XIAO ESP32
  • Accelerometer and Activity Projects
    • Arduino x K-Way - Outdoor Activity Tracker
    • Arduino x K-Way - Gesture Recognition for Hiking
    • Arduino x K-Way - TinyML Fall Detection
    • Posture Detection for Worker Safety - SiLabs Thunderboard Sense 2
    • Hand Gesture Recognition - OpenMV Cam H7
    • Arduin-Row, a TinyML Rowing Machine Coach - Arduino Nicla Sense ME
    • Fall Detection using a Transformer Model – Arduino Giga R1 WiFi
    • Bluetooth Fall Detection - Arduino Nano 33 BLE Sense
    • Monitor Packages During Transit with AI - Arduino Nano 33 BLE Sense
    • Smart Baby Swing - Arduino Portenta H7
    • Warehouse Shipment Monitoring - SiLabs Thunderboard Sense 2
    • Gesture Recognition - Bangle.js Smartwatch
    • Gesture Recognition for Patient Communication - SiLabs Thunderboard Sense 2
    • Hospital Bed Occupancy Detection - Arduino Nano 33 BLE Sense
    • Porting a Posture Detection Project from the SiLabs Thunderboard Sense 2 to xG24
    • Porting a Gesture Recognition Project from the SiLabs Thunderboard Sense 2 to xG24
    • Continuous Gait Monitor (Anomaly Detection) - Nordic Thingy:53
    • Classifying Exercise Activities on a BangleJS Smartwatch
  • Air Quality and Environmental Projects
    • Arduino x K-Way - Environmental Asthma Risk Assessment
    • Gas Detection in the Oil and Gas Industry - Nordic Thingy:91
    • Smart HVAC System with a Sony Spresense
    • Smart HVAC System with an Arduino Nicla Vision
    • Indoor CO2 Level Estimation - Arduino Portenta H7
    • Harmful Gases Detection - Arduino Nano 33 BLE Sense
    • Fire Detection Using Sensor Fusion and TinyML - Arduino Nano 33 BLE Sense
    • AI-Assisted Monitoring of Dairy Manufacturing Conditions - Seeed XIAO ESP32C3
    • AI-Assisted Air Quality Monitoring - DFRobot Firebeetle ESP32
    • Air Quality Monitoring with Sipeed Longan Nano - RISC-V Gigadevice
    • Methane Monitoring in Mines - Silabs xG24 Dev Kit
    • Smart Building Ventilation with Environmental Sensor Fusion
    • Sensor Data Fusion with Spresense and CommonSense
    • Water Pollution Detection - Arduino Nano ESP32 + Ultrasonic Scan
    • Fire Detection Using Sensor Fusion - Arduino Nano 33 BLE Sense
  • Novel Sensor Projects
    • 8x8 ToF Gesture Classification - Arduino RP2040 Connect
    • Food Irradiation Dose Detection - DFRobot Beetle ESP32C3
    • Applying EEG Data to Machine Learning, Part 1
    • Applying EEG Data to Machine Learning, Part 2
    • Applying EEG Data to Machine Learning, Part 3
    • Liquid Classification with TinyML - Seeed Wio Terminal + TDS Sensor
    • AI-Assisted Pipeline Diagnostics and Inspection with mmWave Radar
    • Soil Quality Detection Using AI and LoRaWAN - Seeed Sensecap A1101
    • Smart Diaper Prototype - Arduino Nicla Sense ME
    • DIY Smart Glove with Flex Sensors
    • EdgeML Energy Monitoring - Particle Photon 2
    • Wearable for Monitoring Worker Stress using HR/HRV DSP Block - Arduino Portenta
  • Software Integration Demos
    • Azure Machine Learning with Kubernetes Compute and Edge Impulse
    • ROS2 + Edge Impulse, Part 1: Pub/Sub Node in Python
    • ROS2 + Edge Impulse, Part 2: MicroROS
    • Using Hugging Face Datasets in Edge Impulse
    • Using Hugging Face Image Classification Datasets with Edge Impulse
    • Edge Impulse API Usage Sample Application - Jetson Nano Trainer
    • MLOps with Edge Impulse and Azure IoT Edge
    • A Federated Approach to Train and Deploy Machine Learning Models
    • DIY Model Weight Update for Continuous AI Deployments
    • Automate the CI/CD Pipeline of your Models with Edge Impulse and GitHub Actions
    • Deploying Edge Impulse Models on ZEDEDA Cloud Devices
Powered by GitBook
On this page
  • Introduction
  • What is FOMO
  • Dataset
  • Edge Impulse Project
  • Wrap Up

Was this helpful?

Edit on GitHub
Export as PDF
  1. Computer Vision Projects

Cyclist Blind Spot Detection - Himax WE-I Plus

Use computer vision and a Himax WE-I Plus to detect cyclists in a blind spot on large vehicles.

PreviousShield Bot Autonomous Security RobotNextIV Drip Fluid-Level Monitoring - Arduino Portenta H7

Last updated 1 year ago

Was this helpful?

Created By: Adam Taylor, Adam Fry

Public Project Link:

Introduction

Like many countries, the UK encourages people to cycle, with many cycle paths and cycle-to-work programs. Ideally for safety, the cycle paths are isolated from the main flow of traffic, and my home town of Harlow does pretty well at this.

However, Harlow is new town, and as such these paths could be easily planned and built. In the larger cities and older towns such as London, cyclists have to share the road with other users. Sadly each year this leads to fatalities and injuries on the roads, one case which is especially troublesome is when cyclists are on the inside of large vehicles such as trucks, vans, buses etc. When the cyclist is on the inside of the vehicle and the the vehicle is turning left (or right in the most other countries) there exists the conditions for the driver to not see the cyclist and turn into their path. Unfortunately, this leads to many injuries and deaths.

One of the major cause of these incidents is driver visibility. It is difficult for the driver to see down the side of the vehicle. There are attempts to prevent these events, one thing which is used in London on many large vehicles is a warning to Cyclists that they may be in the blind spot of the vehicle.

Of course, it would be better for a system which would alert the driver that a cyclist was in their blind spot. This led to me thinking about how it could possibly be achieved, and retrofit into vehicles. Ideally the system would be low cost, small, capable of operating off a battery, and able to give a fast and timely warning to the driver. And, the system needs to be nearly self contained.

My idea was to use a little camera, which would be able to raise an indication or alert to the driver that there was a cyclist in the blind spot. Ideally, this would be audible such that the driver could not fail to see it. For this reason I chose the Himax WE-I Plus camera from SparkFun. This device includes a simple grey-scale VGA camera, and has the ability to break out GPIO such that a buzzer or other warning could be generated.

The USB connector can be used for powering the device, and it is small enough to be easily packaged and deployed.

The best way to be able to detect cyclists is to capture an image and analyse if a Cyclist is present. This is a perfect job for machine learning, specifically object detection. As we want to deploy at the edge on a small, power-constrained microprocessor, it is an ideal use case for Edge Impulse and their Faster Objects, More Objects (FOMO) algorithm.

What is FOMO

Image classification, where we say if an item is present in a image, works well as long as there is only a single object in the image.

Alternatively, object detection is able to provide the class, number of objects and positions in the image. This is what we need for cyclist detection as real world conditions mean there will be many objects in the image and there may be several cyclists also in the same image. It is crucial when this occurs we do detect the cyclist, for this reason we need a object detection algorithm.

However, object detection algorithms are very computationally intensive and therefore struggle to be as responsive as necessary for this use case on a microcontroller.

This is where the FOMO algorithm developed by Edge Impulse comes into play, it provides a simplified version of object detection.

Dataset

Like with all ML/AI projects, one of the largest challenges is in collecting a dataset. There is not a large, publicly available, dataset so we started to collect a small dataset to enable training and proof of concept. If the concept works we can create a larger dataset which addresses more conditions such as low light, weather, etc.

The initial dataset used consisted of 100 images of cyclists in different conditions tagged from around the world. These images were collected from open source images available across the web.

Once the dataset is collected we are able to create the Edge Impulse project.

Edge Impulse Project

Once the project is created we need to get started working on it. I had a work experience student with me this week from the local high school. He helped me create the dataset and train the model, we were able to work collaboratively on the project due to Edge Impulse's new collaboration feature.

On the project Dashboard, select "Add collaborator" and in the dialog add in either the username or email of the individual.

Once they are added you can then easily work together on the project. This enabled Adam F. to work on uploading and labelling the dataset, while I attended meetings.

Each of the images is uploaded and labeled with the location of the Cyclist.

As you upload the images you will notice the labeling queue in the data acquisition page displays the number of items to be labeled.

By clicking on the labelling queue you can label each image.

Once the bounding box is drawn around the object, the next step is to enter the label.

Using this view we can work through each of the images which needs to be labeled.

The next step with the data labeled is to create the impulse.

The first step is to add a processing block - Select "Image Processing" block.

Then we can add the processing block.

With the impulse created the next stage is to configure the image processing block. Change the color depth to Black and White.

Select the "Generate Features" tab and click "Generate features".

These are the features which will be taken forward for training in the processing block.

The final stage is to train the model.

Once the model is trained we will see a confusion matrix which shows the performance. The initial training was good but the F1 score (which is the a key indication of the result) was too low.

While it looks good on individual images like below.

When we test it on the entire validation set the accuracy score was very low, only 36%, which is not acceptable.

We can get better performance than this. However, before we change the settings of the project, we will save a version of it. This will allow us to save the current state of the project, so we have a version we can revert to if necessary.

With the version saved, the next step is to change some of the project settings. Investigating the project settings, the generated features are not closely clustered.

We can change the setting on the resize, to resize with respect to the longest side.

Regenerating the features shows a much closer clustering in the Feature Explorer.

I also changed the number of training cycles, and the learning rate. This resulted in a better F1 score, though a slightly reduced accuracy compared to previously.

This resulted in much better performance when tested against the validation set.

To deploy the algorithm on the target hardware we select "Deploy" and choose the Himax WE-i Plus camera from the options.

This will generate an application directly for the target device.

To load the application onto the Himax WE-I Plus, extract the downloaded folder and run the batch file for your operating system, then follow the on-screen commands.

Press the Reset button on the device when instructed.

With the application flashed, we are able to run some tests using the camera against images, using live Classification.

Both images were correctly identified as a cyclists.

Second classification:

The final step is run the application on the board, standalone. Testing this against a range of images shows cyclists detected.

Wrap Up

This project shows that tinyML can be used in a small microcontroller-based image processing system to detect cyclists. This approach can be further developed to produce a system which can be used to increase road safety.

The first thing do is log into your and create a new project.

Edge Impulse account
https://studio.edgeimpulse.com/public/108632/latest
Cycle Paths for my home town of Harlow, UK