RT-DETR-Smoke: A Real-Time Transformer for Forest Smoke Detection

Abstract

Smoke detection is crucial for early fire prevention and the protection of lives and property. Unlike generic object detection, smoke detection faces unique challenges due to smoke’s semitransparent, fluid nature, which often leads to false positives in complex backgrounds and missed detections—particularly around smoke edges and small targets. Moreover, high computational overhead further restricts real-world deployment. To tackle these issues, we propose RT-DETR-Smoke, a specialized real-time transformer-based smoke-detection framework. First, we designed a high-efficiency hybrid encoder that combines convolutional and Transformer features, thus reducing computational cost while preserving crucial smoke details. We then incorporated an uncertainty-minimization strategy to dynamically select the most confident detection queries, further improving detection accuracy in challenging scenarios. Next, to alleviate the common issue of blurred or incomplete smoke boundaries, we introduced a coordinate attention mechanism, which enhances spatial-feature fusion and refines smoke-edge localization. Finally, we propose the WShapeIoU loss function to accelerate model convergence and boost the precision of the bounding-box regression for multiscale smoke targets under diverse environmental conditions. As evaluated on our custom smoke dataset, RT-DETR-Smoke achieves a remarkable 87.75% [email protected] and processes images at 445.50 FPS, significantly outperforming existing methods in both accuracy and speed. These results underscore the potential of RT-DETR-Smoke for practical deployment in early fire-warning and smoke-monitoring systems.

Topics & Concepts