Research on fire detection based on deformable multi-scale attention

Rapid and effective identification of flame smoke at the early stage of a fire in a home kitchen scenario is crucial for reducing casualties and property losses. In this paper, a deformable multi-scale attention-based detection model is proposed for the detection of flame and smoke targets, which has the problems of unfixed morphology and variable features. The model adopts deformable convolutional kernel and deformable attention to enhance the feature extraction ability of the backbone network for the target, and utilizes progressive pyramid network based on adaptive spatial feature fusion and efficient multiscale attention mechanism to enhance the multiscale feature fusion ability of the model. The final experimental results show that the detection accuracy of our model improves by 3.7% when compared with the best benchmark accuracy based on Yolov8s. It proves that our method can effectively detect targets such as flame smoke and prevent fires more reliably.