- To collect and annotate a large-scale dataset of fire and smoke images and videos from various sources and scenarios.
- To design and implement a deep neural network model that can accurately classify fire and smoke in random images and videos.
- To evaluate the performance of the model on various metrics, such as Precision, Recall, Mean Average Precision, F1 score, Confidence etc. and curves formed using these parameters
- To provide real-time fire and smoke detection from camera feeds or user uploads.
YOLO v5 | YOLO v8 |
The initial challenge we encountered was selecting the most appropriate model for small base object detection models using around <500 images as a dataset, which we discovered to be YOLO as the best among the open source models.
Subsequently, we experienced many false detections and inaccurate results, which we attempted to minimize by creating more refined labels. After successfully training the model on Fire and Smoke we decided to add new labels such as Fire Alarm, Fire Exit, Fire Extinguishers, FireMen, etc. in the model, but the False detection of the video increased dramatically.
We tried to train the model individually on these new labels and figured that the Fire Extinguisher dataset caused the problem until we removed it from the dataset.