*Click on image to view the current detection model tested on test videos. Alternatively: https://youtu.be/svnkKFeQUZs
This project aims to study how different state-of-the-arts detection / segmentation models would perform on our smoke data obtained from fire tower cameras around the ACT. The goal is to apply both weakly-supervised learning with bounding-box labels.
The dataset features 2686 bounding-box labelled smoke images, 1879 has been assigned for training, 393 for validation and 396 for testing.
⛔️ Please note this model is a work-in-progress and is not suitable for use as a fail-safe bushfire detection system. *Note (23/5/2022): There is currently issues with uploading the 'outputs' folder containing my trained model and an mp4 test video showcase due to its large file size.
I first extracted the smoke footages as frames and labelled them individually on MATLAB using the image labeller tool in the toolbox.
Then, after obtaining a matrix of gTruth box values, I converted each row of the matrix as its own individual .txt file and saved them at data/boxLabels.
After that, applying creat_coco.py to create a coco-json formatted dataset.
This will allow easier data application to other existing models.
However, I ended up using the roboflow website to generate xml files for processing. Labelled smoke data available here: https://app.roboflow.com/honours/deep-smoke-detection/2
Below shows an example of a labelled image for training.
This part of the code can be used for custom coco dataset creation, feel free to replace data folder with your own set of testing, training and box labels.
My training, modelling and testing codes follow closely to the tutorial on custom object detection with Faster-RCNN by Sovit Ranjan RathSovit Ranjan Rath. Work-to-date had shown that there in still much more improvement to be made with the model. While some smoke are detected, a lot of the times when a smoke is spread out or relatively zoomed-in, a detection was failed to make. I would like to continue training the model to obtain better results. Below shows examples of detected smoke:
The test video is available for download on Google drive: https://drive.google.com/file/d/1-mYKqr6uHP8bZw3fsxCxol_EC5ior3r6/view?usp=sharing
I would like to acknowledge Sovit Ranjan Rath's custom object detection tutorial on Fast-RCNN on the development of this project https://debuggercafe.com/custom-object-detection-using-pytorch-faster-rcnn/