An efficient fire detection network with enhanced multi-scale feature learning and interference immunity

Effective fire detection can identify the source of the fire faster, and reduce the risk of loss of life and property. Existing methods still fail to efficiently improve models' multi-scale feature learning capabilities, which are significant to the detection of fire targets of various sizes. Besides, these methods often overlook the accumulation of interference information in the network. Therefore, this paper presents an efficient fire detection network with boosted multi-scale feature learning and interference immunity capabilities (MFII-FD). Specifically, a novel EPC-CSP module is designed to enhance backbone's multi-scale feature learning capability with low computational consumption. Beyond that, a pre-fusion module is leveraged to avoid the accumulation of interference information. Further, we also construct a new fire dataset to make the trained model adaptive to more fire situations. Experimental results demonstrate that, our method obtains a better detection accuracy than all comparative models while achieving a high detection speed for video in fire detection task.