Forest-Fire-Detection

CNN based Forest Fire Detection for camera equipped edge devices.

Recent escalation in Wildfire events have posited a need of early Fire/Smoke detection systems. While state-of-the-art approaches in this domain are concentrated on video-based solutions, the complexity of such algorithms is indisputably higher. A single pass, image-based algorithm could be easily deployed on edge devices with minimal cost and resources.

Initial focus of this project is to achieve comparable results on a proposed dataset, resolving all the known corner cases, which will then incline towards retaining the achieved performance with efficient architectures.

Dataset is created by extracting frames from YouTube videos, querying google images and aggregating from various sources such as dataturks.

Some of the challenging test-cases I'm trying to address are:

Cloud vs Smoke	Sunset vs Fire

Initial dataset was having only 471 images in total, hence lead to better results (90+). However, after identifying the corner cases and aligning dataset with them, I've diverse, more challenging distribution as follows:

Class	Examples
Cloud	400
Fire	755
Smoke	564
Non-fire	754
Total	2473

Todos:

Baseline notebooks in Keras, fastai and fastai2 (current experiments)
Deployment pipeline for Raspberry Pi (using tflite)
Implement Multilabel Classification
Multiclass to binary mapping

Results

Model Name	Error Rate	Precision	Recall	F1 Score
xresnet50-sa-mish-r128	25.49	74.51	74.51	74.51
xresnet50-sa-mish-r192	18.90	81.10	81.10	81.10

sa- self attention, mish- activation function, r-resolution

Higher resolutions are improving the results

Confusion matrix (xresnet50-sa-mish-r192):

Top Losses:

Observations:

Clearly the model is confused between like classes and some of them can be ignored. For instance, predicting a non-fire image as cloud or smoke as fire is acceptable and model should focus on distinguishing the counterparts of them.

Mapping Multiclass predictions to binary could lead to better accuracy.
Loss function customized for this scenario could improve the performance, although it might make the model too specific.