Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment
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 t...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1503 |
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| author | Zhiyuan Yang Suchang Cao Michal Aibin |
| author_facet | Zhiyuan Yang Suchang Cao Michal Aibin |
| author_sort | Zhiyuan Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9ff50e5b574e4bb095de36cf77ab7935 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9ff50e5b574e4bb095de36cf77ab79352025-08-20T03:52:57ZengMDPI AGRemote Sensing2072-42922025-04-01179150310.3390/rs17091503Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk AssessmentZhiyuan Yang0Suchang Cao1Michal Aibin2Department of Computing, British Columbia Institute of Technology, 555 Seymour St., Vancouver, BC V6B 3H6, CanadaDepartment of Computing, British Columbia Institute of Technology, 555 Seymour St., Vancouver, BC V6B 3H6, CanadaDepartment of Computing, British Columbia Institute of Technology, 555 Seymour St., Vancouver, BC V6B 3H6, CanadaForest 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.https://www.mdpi.com/2072-4292/17/9/1503computer visionUAVsRGBLog RGBforest firesobject detection |
| spellingShingle | Zhiyuan Yang Suchang Cao Michal Aibin Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment Remote Sensing computer vision UAV sRGB Log RGB forest fires object detection |
| title | Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment |
| title_full | Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment |
| title_fullStr | Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment |
| title_full_unstemmed | Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment |
| title_short | Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment |
| title_sort | beyond srgb optimizing object detection with diverse color spaces for precise wildfire risk assessment |
| topic | computer vision UAV sRGB Log RGB forest fires object detection |
| url | https://www.mdpi.com/2072-4292/17/9/1503 |
| work_keys_str_mv | AT zhiyuanyang beyondsrgboptimizingobjectdetectionwithdiversecolorspacesforprecisewildfireriskassessment AT suchangcao beyondsrgboptimizingobjectdetectionwithdiversecolorspacesforprecisewildfireriskassessment AT michalaibin beyondsrgboptimizingobjectdetectionwithdiversecolorspacesforprecisewildfireriskassessment |