Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment

Abstract

Forest fire risk assessment and prevention are crucial topics in environmental management. The most popular method involves using drone imagery and object detection models to analyze risk. However, traditional drone images typically use the sRGB color space, which may lose valuable information. In this study, we systematically investigate the impact of different color spaces (sRGB, Linear RGB, Log RGB, XYZ, LMS, and D-Log) on the performance of state-of-the-art vision transformer models and the latest YOLO model for tree condition detection. Our experiments demonstrate that Log RGB and Linear RGB significantly outperform the conventional sRGB color space, with Log RGB achieving a 27.16% improvement in mean average precision (mAP) and a 34.44% gain in mean average recall (mAR). These improvements are attributed to Log RGB’s enhanced dynamic range, superior illumination invariance, and better information preservation, which enable the detection of subtle environmental details crucial for early wildfire risk assessment. Overall, our findings highlight the potential of leveraging alternative color space representations to develop more accurate and robust tools for wildfire risk assessment.

Code and data availability

The supplied blocks describe a UAV tree-condition dataset (335 RAW DNG images) and color-space conversion pipelines, but contain no public deposit, availability statement, or authors' URL for the dataset, images, code, or trained models. No paper-specific public asset is actionable.

No evidence-backed public reproduction asset is currently recorded.