## 2 Materials and Methods
### Fire Prevention and Fire Detection
There is frequently a need for an accurate, quick, and portable fire-detection solution that can be used on hardware with constrained computational power and that is also reasonably priced. Since GoogleNet is better suited for implementation on FPGA and other memory-constrained hardware while maintaining good classification accuracy, the authors presented a low-cost fire-detection CNN architecture based on it in [18]. Two primary-convolution layers, four max-pooling levels, one average-pooling layer, and seven inception layers make up the 100 layers of the suggested model. They also employed a transfer-learning strategy in this work. In terms of accuracy, the experiments produced great results when compared to more robust models like AlexNet. Another approach to consider when it comes to fire prevention and fire detection is using infrared thermal (IRT) cameras. The benefit of this method is its capacity for early fire detection and prevention because it can identify anomalies before other symptoms manifest (smoke or smell), which can be seen in Figure 4.  
For instance, an electrical installation that is overheating because of an overload or a faulty connection may catch fire eventually. It is feasible to recognize an increase in temperature and respond appropriately by using thermal imagery. In [19], scientists used CNN and IRT images of rotating machinery to extract fault features. Then, fault-pattern identification was carried out using the Softmax Regression classifier. The proposed method had outstanding performance in detecting and recognizing various defects on bearings and rotors during the nine different types of faults that were used to assess the system's effectiveness. IRT images were used in [20] to find electrical-facility flaws. As detection methods, Fast Region-Based CNN (Fast R-CNN), Faster R-CNN, and YOLOv3 were employed. The detected objects were examined using a thermal-intensity-area analysis (TIAA). The results were most accurate when using a Faster R-CNN. According to the authors of [21], transforming images in the HSV color space produces better results than other color schemes such as grayscale. They employed the [PERSON], Prewitt, and [PERSON] techniques for thresholding. A loose phase connection, an imbalanced phase connection, an overloaded phase connection, and a solar panel with a fault were used as test cases for their hot-region-detection approach. Their suggested approach successfully identified overheating on the device. Although every kind of approach brings an advantage for a specific use-case, none of the abovementioned approaches or other commercially available solutions are ideal for  
Figure 4: Fire-detection systems in relation to time and required size of fire before triggering [24]. FireBot. Due to the specific use-case, FireBot should navigate in an indoor closed area that is changing dynamically and at the same time be able to detect fire using a visual camera. It should also be able to prevent potential fire outbreaks by using an infrared thermal camera along with various other sensors and extinguish fire if detected. To be able to do all that autonomously and efficiently, a new solution that is a fusion of multiple approaches, specially designed for FireBot's use-case, is being developed. Some of the considered approaches are described in the following subsection, and the final solution is still in development.