Schlagwort: Fire Detection

  • This Nicla Vision-based fire detector was trained entirely on synthetic data

    This Nicla Vision-based fire detector was trained entirely on synthetic data

    Reading Time: 2 minutes

    Due to an ever-warming planet thanks to climate change and greatly increasing wildfire chances because of prolonged droughts, being able to quickly detect when a fire has broken out is vital for responding while it’s still in a containable stage. But one major hurdle to collecting machine learning model datasets on these types of events is that they can be quite sporadic. In his proof of concept system, engineer Shakhizat Nurgaliyev shows how he leveraged NVIDIA Omniverse Replicator to create an entirely generated dataset and then deploy a model trained on that data to an Arduino Nicla Vision board.

    The project started out as a simple fire animation inside of Omniverse which was soon followed by a Python script that produces a pair of virtual cameras and randomizes the ground plane before capturing images. Once enough had been created, Nurgaliyev utilized the zero-shot object detection application Grounding DINO to automatically draw bounding boxes around the virtual flames. Lastly, each image was brought into an Edge Impulse project and used to develop a FOMO-based object detection model.

    By taking this approach, the model achieved an F1 score of nearly 87% while also only needing a max of 239KB of RAM and a mere 56KB of flash storage. Once deployed as an OpenMV library, Nurgaliyev shows in his video below how the MicroPython sketch running on a Nicla Vision within the OpenMV IDE detects and bounds flames. More information about this system can be found here on Hackster.io.

    [youtube https://www.youtube.com/watch?v=OFCwgWvivHo?feature=oembed&w=500&h=375]

    The post This Nicla Vision-based fire detector was trained entirely on synthetic data appeared first on Arduino Blog.

    Website: LINK

  • Using sensor fusion and tinyML to detect fires

    Using sensor fusion and tinyML to detect fires

    Reading Time: 2 minutes

    The damage and destruction caused by structure fires to both people and the property itself is immense, which is why accurate and reliable fire detection systems are a must-have. As Nekhil R. notes in his write-up, the current rule-based algorithms and simple sensor configurations can lead to reduced accuracy, thus showing a need for more robust systems.

    This led Nekhil to devise a solution that leverages sensor fusion and machine learning to make better predictions about the presence of flames. His project began with collecting environmental data consisting of temperature, humidity, and pressure from his Arduino Nano 33 BLE Sense’s onboard sensor suite. He also labeled each sample either Fire or No Fire using the Edge Impulse Studio, which was used to generate spectral features from the three time-series sensor values. This information was then passed along to a Keras neural network that had been configured to perform classification, resulting in an overall accuracy of 92.86% when run on real world test samples.

    Confident in his now-trained model, Nekhil deployed his model as an Arduino library back to the Nano 33 BLE Sense. The Nano sends a message over its UART pins to an awaiting ESP8266-01 board when a fire has been detected. And in turn, the ESP8266 triggers an IFTTT webhook to alert the user via an email.

    If you would like to learn more about the construction of this fire recognition system, plenty of details can be found on the project page.

    The post Using sensor fusion and tinyML to detect fires appeared first on Arduino Blog.

    Website: LINK