Work

DETR-AE-Mamba: A lightweight and accurate detection framework for early fire warning

Year: 2026

Type: article

Abstract: Fire disasters pose severe threats to human lives, ecological systems, and economic assets worldwide, making early and accurate detection crucial for timely disaster response. While deep learning-based methods have shown promise for fire monitoring, existing approaches face persistent challenges: edge blurring caused by smoke and adverse weather degrades localization accuracy, small fire sources are frequently missed due to insufficient pixel information, and flame-like backgrounds cause false alarms. Current CNN+Transformer architectures struggle to address these issues simultaneously while maintaining computational efficiency. To bridge this gap, we propose De tection Tr ansformer- A daptive E ncoder - Mamba (DETR-AE-Mamba), a lightweight detection framework with three novel components: Mamba-P erceptron (Mamba-P), which processes features in both time and frequency domains to enhance edge extraction; A daptive F usion E ncoder (AFE), which improves global perception for small object detection; and ELD-Decay-Varifocal-Loss , a dynamic loss function that enhances noise robustness. Experiments on the D-Fire dataset show that DETR-AE-Mamba improves precision by 3.4% over RT-DETR-R18 while reducing parameters by 54.2% (from 21.4M to 9.8M) and GFLOPs by 69.8% (from 56.9 to 17.2), demonstrating potential for fire detection and applications in other fields.

Source: Journal of King Saud University - Computer and Information Sciences

DOI: 10.1007/s44443-026-00557-1