Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery

TL;DR

This paper improves UAV-based forest fire risk assessment by integrating advanced multi-stage object detection algorithms and color encoding optimizations, demonstrating enhanced accuracy in real-world aerial imagery.

Contribution

It introduces a multi-stage detection framework with various CNN-based detectors and optimizations like CBAM and color space adjustments for better fire detection accuracy.

Findings

Multi-stage detectors significantly improve fire detection accuracy.

Color encoding and attention mechanisms enhance model performance.

Extensive experiments validate the framework's effectiveness in real-world scenarios.

Abstract

Forest fires pose a significant threat to ecosystems, economies, and human health worldwide. Early detection and assessment of forest fires are crucial for effective management and conservation efforts. Unmanned Aerial Vehicles (UAVs) equipped with advanced computer vision algorithms offer a promising solution for forest fire detection and assessment. In this paper, we optimize an integrated forest fire risk assessment framework using UAVs and multi-stage object detection algorithms. We introduce improvements to our previous framework, including the adoption of Faster R-CNN, Grid R-CNN, Sparse R-CNN, Cascade R-CNN, Dynamic R-CNN, and Libra R-CNN detectors, and explore optimizations such as CBAM for attention enhancement, random erasing for preprocessing, and different color space representations. We evaluate these enhancements through extensive experimentation using aerial image footage from various regions in British Columbia, Canada. Our findings demonstrate the effectiveness of multi-stage detectors and optimizations in improving the accuracy of forest fire risk assessment. This research contributes to the advancement of UAV-based forest fire detection and assessment systems, enhancing their efficiency and effectiveness in supporting sustainable forest management and conservation efforts.