No need to be afraid of intelligent machines! They keep you safe from fire

Overall procedure

The method devised by Zhao et al. aims to detect fire with a high accuracy rate, low false alarm rate, and rapid detection. In fact, they combined both image classification and object detection methods to detect fire. The overall suggested procedure includes three major steps: Motion detection, Fire detection, and Region classification (Figure 1).

In the first step, footage from surveillance cameras is processed to subtract the background using OpenCV, an open-source computer vision library, and detect moving objects instead of using the CNN computation, which is quite resource hungry. Only if moving objects are detected, the next steps will be executed (Figure 2). The background subtraction includes four steps: First, eliminate image noise with the Gaussian blur method (Figure 2a); second, capture the moving object (e.g.  fire or humans) using a KNN-based model (Figure 2b); third, convert gray regions to white using a binarization method (Figure 2c); and in the end, combine little discrete regions to create a uniform  region using the closing operation (Figure 2d). During the whole process, the size of the white region is calculated and if it  exceeds the determined threshold, subsequent  steps will be executed.

In the Next  step, the fire region is recognized using a CNN-based fire detection model instead of using the hand-designed feature extractors or the slide window methods for the region of interest (ROI) generation. The input and output of this model are images and predictive labels, respectively. There are two categories for object detections, i.e., one-stage and two-stage methods. Methods from the first category, like YOLO (You only look one), SSD, etc., approach the task as a regression problem. The latter category methods are  based on region proposals, such as R-CNN, Fast R-CNN, Faster R-CNN, etc. The main differences between these two methods are the needed time and consumed computing resources, which are significantly higher in two-stage methods. Thus, one-stage methods work much faster but with slightly lower accuracy compared to the two-stage methods. Dr. Zhao et al., employed a YOLO method for object detection (Figure 1).

However, there is a possibility that some objects similar to  fire will be  incorrectly labeled  as fire regions. To prevent false alarms, they used a region classification model to check in real-time if the recognized region is really on  fire or not. An alarm will be sent to safety technicians as soon as a fire is confirmed, giving them important details such as the location of the fire and its size, to help extinguish the fire faster (Figure 1).

The entire fire image dataset contains 5075 images with various widths and heights. The training set is 80%, i.e. 4060 images and 1015 images are used as the testing set. Each image may include one or several fire regions annotated by a bounding box (Figure 3).