Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
Fire Detection model is a YOLOv5 based model. It runs real-time and can be deployed on cameras, drones, IoT devices, and other surveillance systems to enhance fire safety measures. It is based on the small version of the model.
Shared by:
Model type: Computer vision
License:
Training Details
It was trained on fire images from .
Testing Details
No testing details available.
Technical Specifications
Input/Output Details
Input:
Name: images
Info: NCHW BGR un-normalized image
Output1:
Name: output1_yolov5
Info: Detection output 1
Output2:
Name: output2_yolov5
Info: Detection output 2
Output3:
Name: output3_yolov5
Info: Detection output 3
Model Architecture
Backbone: CSPDarknet53 - Extracts meaningful features from the input image, such as edges, textures, and object structures.
Neck: PAN - Enhances feature maps for detecting objects of different sizes.
Head: Outputs final detection results, including bounding boxes, confidence scores, and class labels.
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Utilization
Models converted for RVC Platforms can be used for inference on OAK devices.
DepthAI pipelines are used to define the information flow linking the device, inference model, and the output parser (as defined in model head(s)).
Below, we present the most crucial utilization steps for the particular model.
Please consult the docs for more information.
The model is automatically parsed by DAI and it outputs the
dai.ImgDetections message (bounding boxes and scores of the detected fire).
Get model output(s):
while pipeline.isRuning():
nn_output: dai.ImgDetections = parser_output_queue.get()
Example
You can quickly run the model using our script.
It automatically downloads the model, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
To try it out, run:
