  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.
