# Computer Vision for Fire Detection on UAVs--From Software to Hardware
## 4 Taxonomy
### Software/Method  
Artificial intelligence models for image classification and recognition could not be missing from such applications. [PERSON], [PERSON], [PERSON] and [PERSON] present in their paper an object detector for smoke and fire detection based on Faster R-CNN [34]. The corresponding ROC curve shows great results above 90%. It is noted here that prediction results of the fire detection were slightly better than the smoke detection. As expected, a lot of images were required for training and testing purposes. In order for the training to be more efficient, the images have to be different but similar. Sometimes, the model performance will be reduced if the images failed to match the quality and quantity criteria. The aforementioned object detector was applied by the same scientific team to a UAV platform for Early Forest Fire Detection [29]. Faster R-CNN had also great performance and a threshold of 90% accuracy was applied. The threshold was used as a proposed trigger and when the possibility of fire detection was over 90%, the system recognized an emergency and sent a notification to the authorities. Object detectors in this specific area are the You Only Look Once model (YOLO) and the Single Shot MultiBox Detector (SSD). Three applications used the YOLOv3 [48; 49; 50]. YOLOv3 shows great adaptability achieving very high performance. When using the precision as a metric, the model's accuracy was over 90%. However, in the application of [PERSON], [PERSON], and [PERSON] [48], the observation of the recall and F1 metrics reveals a YOLO weakness achieving very low results (50%). They manage to overcome this hurdle by creating a novel model for flame and smoke signatures using a combination of a proposed local binary pattern alongside an Artificial Neural Network (ANN) and a Support Vector Machine (SVM). The SSD [40], in this specific application, was a group of MobilNets. The reason for this architecture was to gain benefits from the MobileNet characteristics achieving great prediction results with low latency. There are also other CNN approaches for the fire detection problem. [PERSON] with [PERSON] [51], [PERSON] with [PERSON] and [PERSON] [52] and [PERSON] with [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [40] used pre-trained CNN models and, more specifically, the VGG-16, Resnet34, Resnet50, U-Net and MobileNet. The advantages of a pre-trained CNN model is that when it is applied to an experimental problem, it can achieve great prediction results. However, when they are used for real-world problems, their results have problems regarding the number of false negatives or positives. Furthermore, [PERSON] and [PERSON] selected another approach, thus creating new CNN models instead of using pre-trained versions of popular CNN's [20; 51]. These are the ERNet and the Emergency Net. Both models were designed for low power consumption and computational needs matching UAV special characteristics. Additionally, they have been trained on the same dataset such as the AIDER and achieved similar results compared to the traditionally powerful CNN such as VGG-16 and ResNet50, but with less computational cost. More specifically, ERNet achieved an average accuracy of 90.1% with 18.7 ms latency compared to VGG-16 and ResNet50 with 91.9% (346 ms) and 90.2% (257 ms), respectively. The Emergency Net F1 score was 95.7% with 57 \(\times\) 10\({}^{6}\) FLOPS comparing also with VGG-16 and ResNet50 with 96.4% (17,620\({}^{6}\) FLOPS) and 96.1% (4533 \(\times\) 10\({}^{6}\) FLOPS). Besides the above, Fuzzy Systems were also used in [41]. Their approach was by fusing the images from two cameras and reducing noise from vibrations. As a result, they obtained improved accuracy of the fire detection. In addition, a proposed Optimal Residual Network-Based Features Extraction algorithm (O-RNBFE) for feature extraction and a Latent Variable Support Vector Machine (LVSVM) for classification can be used for fire detection.