This project is about the development of smoke detection algorithms in case of forest fires. Our main problem statement is applying classification and object detection to identify smoke / fire through images I am using Mask RCNN model for object detection in this case.

Step 1: Data gathering and collection: We have gathered data available online at below location. https://sintecsys-omdena.s3.amazonaws.com/images3.zip Dataset have almost 16.5K images (~8k masked and ~8k original images).


Step 2: Exploration of data: Masked images have been already created. The model needs to be trained on existing masked images and start predicting. Size of images is: 1920 X 1080 
 Step 3: Applying Image recognition and Object detection models.

Iteration 1: Simple image recognition model and tried to train with simple CNN model

Iteration 2: Alexnet model for classification

Iteration 3: Mask RCNN model

Step 4: Demonstration of Mask RCNN model

a. Overview of Mask RCNN:

b. Training and Validation Loss:

c. Precision and Recall numbers:

d. Result and comparison with Unet Masks:

Step 5: Demonstation using Flask and HTML / Javascript UI:

Step 6: Generating Alerts: Generating alerts through emails