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Deep learning-based forest fire detection using an improved SSD algorithm with CBAM
Diansheng Zhang1, Yueyuan Zhang1, Leilei Dong2
1School of Information and Software Engineering, East China Jiaotong University, Nanchang, China.
Plos One
|November 18, 2025
Summary
A new CBAM-SSD model improves forest fire detection by enhancing flame and smoke recognition. This object detection system offers higher accuracy and fewer false alarms for real-time wildfire monitoring.
Area of Science:
- Computer Science
- Environmental Science
- Artificial Intelligence
Background:
- Forest fires pose significant ecological threats due to rapid spread and destructive potential.
- Existing forest fire detection systems struggle with variable flame/smoke features and environmental interference, leading to false positives and missed detections.
Purpose of the Study:
- To develop a novel object detection model, CBAM-SSD, for improved forest fire detection.
- To enhance the accuracy and reliability of detecting flames and smoke in complex environmental conditions.
Main Methods:
- Employed data augmentation (geometric, color transformations) to address data limitations.
- Integrated the CBAM module into the SSD backbone for adaptive feature extraction, focusing on critical fire regions.
- Reconstructed the feature extraction structure to improve perception of variable flame and smoke characteristics.
Main Results:
- CBAM-SSD achieved a mAP@0.5 of 97.55% for flames and smoke, a 1.53% improvement over baseline SSD.
- Flame detection AP50 reached 96.61% (3.01% increase) with 96.40% recall.
- Smoke detection AP50 reached 98.49% with 98.80% recall, demonstrating superior accuracy and reduced detection errors.
Conclusions:
- The CBAM-SSD model is lightweight, suitable for real-time forest fire detection.
- The model significantly improves detection accuracy and reduces false and missed detections.
- Offers an efficient, convenient, and accurate solution for real-time forest fire monitoring.
