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The development of robust and efficient computational methodologies is crucial for vision-based flame detection systems. While deep learning has shown promise, many existing models are direct applications of generic architectures, lacking principled methodologies to address the inherent physical characteristics of flame, such as heat diffusion and multi-scale radiative patterns. To bridge this gap, this paper proposes FlameDet, a novel flame detection framework grounded in physics-inspired computing and multi-resolution analysis. Unlike conventional approaches, FlameDet formulates visual feature propagation through the lens of heat conduction physics. The core contribution is the Heat Diffusion Module, a computationally efficient backbone that explicitly models feature spread by solving a parameterized heat equation via discrete cosine transform. This physics-aligned design achieves a global receptive field with O (N1. 5) complexity, processing high-resolution inputs 2× faster with 54% less memory than RT-DETR-ResNet50, while providing an interpretable computational process. Furthermore, a High–Low Frequency Analysis module is proposed, a multi-resolution computational strategy that decomposes features into low-frequency components for global context and high-frequency components for fine-grained details. To enhance contextual reasoning for small flames without computational penalty, a DSKC3 module that employs dilated convolutions and structural re-parameterization is designed, expanding the receptive field and by 26. 5%. Extensive experiments on FlameLife dataset demonstrate that FlameDet establishes a new state-of-the-art, improving the F1-score and AP50 by 3. 5% and 4. 0%, respectively, while maintaining superior efficiency.
Han et al. (Thu,) studied this question.