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Research Article Open Access

Using Combination Methods To Improve Real Time Forest Fire Detection

Abstract

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, we utilize PCA to extract features. Results showed that PCA with YOLOv3 increased the mean average precision (mAP) and the detection accuracy by 3.3% and 16.3% separately. PCA with SSD increased mAP and detection accuracy by 1% and 2.1% separately. These results suggest PCA to be a robust tool for improving different objective detection networks. This paper is very practical for forest safety and real time forest monitor.

Shixiao Wu and Chengcheng Guo

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