About Projects

Early detection of Forest and Land Fires (FLF) is essential to prevent the rapid spread of fire as well as minimize environmental damage. However, accurate detection under real-world conditions, such as low light, haze, and complex backgrounds, remains a challenge for computer vision systems. This study evaluates the impact of three image enhancement techniques—Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and a hybrid method called DBST-LCM CLAHE—on the performance of the YOLOv11 object detection model in identifying fires and smoke. The D-Fire dataset, consisting of 21,527 annotated images captured under diverse environmental scenarios and illumination levels, was used to train and evaluate the model. Each enhancement method was applied to the dataset before training. Model performance was assessed using multiple metrics, including Precision, Recall, mean Average Precision at 50% IoU (mAP50), F1-score, and visual inspection through bounding box results. Experimental results show that all three enhancement techniques improved detection performance. HE yielded the highest mAP50 score of 0.771, along with a balanced precision of 0.784 and recall of 0.703, demonstrating strong generalization across different conditions. DBST-LCM CLAHE achieved the highest Precision score of 79%, effectively reducing false positives, particularly in scenes with dispersed smoke or complex textures. CLAHE, with slightly lower overall metrics, contributed to improved local feature detection. Each technique showed distinct advantages: HE enhanced global contrast; CLAHE improved local structure visibility; and DBST-LCM CLAHE provided an optimal balance through dynamic block sizing and local contrast preservation. These results underline the importance of selecting preprocessing methods according to detection priorities, such as minimizing false alarms or maximizing completeness. This research does not propose a new model architecture but rather benchmarks a recent lightweight detector, YOLOv11, combined with image enhancement strategies for practical deployment in FLF monitoring. The findings support the integration of preprocessing techniques to improve detection accuracy, offering a foundation for real-time FLF detection systems on edge devices or drones, particularly in regions like Indonesia.

Keywords: Histogram equalization; YOLO; forest and land fire detection; deep learning



Click to view article
Click to download project (Github)

APA Style
Dewi, C., Santoso, M.V.V., Chernovita, H.P., Mailoa, E., Philemon, S.A. et al. (2025). Integration of YOLOv11 and Histogram Equalization for Fire and Smoke-Based Detection of Forest and Land Fires. Computers, Materials & Continua, 84(3), 5361–5379. https://doi.org/10.32604/cmc.2025.067381

Vancouver Style
Dewi C, Santoso MVV, Chernovita HP, Mailoa E, Philemon SA, Chen APS. Integration of YOLOv11 and Histogram Equalization for Fire and Smoke-Based Detection of Forest and Land Fires. Comput Mater Contin. 2025;84(3):5361–5379. https://doi.org/10.32604/cmc.2025.067381

IEEE Style
C. Dewi, M. V. V. Santoso, H. P. Chernovita, E. Mailoa, S. A. Philemon, and A. P. S. Chen, “Integration of YOLOv11 and Histogram Equalization for Fire and Smoke-Based Detection of Forest and Land Fires,” Comput. Mater. Contin., vol. 84, no. 3, pp. 5361–5379, 2025. https://doi.org/10.32604/cmc.2025.067381

Wildfire detection is challenging due to the subtle and variable appearance of smoke and fire in complex en-vironments. This study aims to improve detection performance by integrating YOLOv12, which includes the R-ELAN backbone and FlashAttention, with histogram-based preprocessing techniques. Three methods were evaluated on the D-Fire dataset: Histogram Equalization (HE), Contrast-Limited Adaptive Histogram Equali-zation (CLAHE), and Dynamic Block Size Technique with Local Contrast Modification CLAHE (DBST-LCM CLAHE). The methodology involved training YOLOv12N on enhanced datasets and assessing performance through precision, recall, mAP, loss curves, and qualitative detection. Results show that CLAHE-enhanced YOLOv12N achieved the best performance with 84.4% precision, 78.5% recall, and 86.7% mAP@0.5, surpas-sing the baseline model (78.8%, 73.0%, 80.1%). DBST-LCM CLAHE ranked second, offering improvements in low-contrast conditions but reducing fine details, while HE performed worst due to noise amplification. This research concludes that CLAHE provides the most consistent and reliable enhancement for wildfire detection. The findings imply that preprocessing plays a key role in improving deep learning–based detection under dif-ficult visual conditions. Future work will focus on adaptive hybrid preprocessing and real-time video-based deployment to strengthen early wildfire detection systems.

Keywords: Forest and land fires; wildfire detection; deep learning; fire and smoke detection; histogram equalization; CLAHE; DBST-LCM CLAHE; YOLOv12



Click to download project (Github)