Using Pcaand One-Stage Detectors For Real-Time Forest Fire Detection

This study investigated the potential for using principal component analysis (PCA) to improve real-time forest fire detection with popular algorithms, such as YOLOv3 and SSD. Before YOLOv3/SSD training, the authors utilised PCA to extract features. Results showed that PCA with YOLOv3 increased the mean average precision (mAP) by 7.3%. PCA with SSD increased the mAP by 4.6%. These results suggest that PCA to be a robust tool for improving different objective detection networks.