Smoke target detection based on dynamic multi-frame decimation with cavity convolution

Compared with traditional smoke alarms, smoke detection based on video images has the advantages of low cost, fast response time, wide applicability, etc. Smoke detection algorithms are becoming an effective and widely used tool for early warning of fires by recognizing smoke graphic features. In this paper, we use the VGG-16 neural network as the benchmark network and improve it by using null convolution and combining it with the dynamic multi-frame subtraction method for smoke detection. The experimental results show that the proposed method is useful for early warning of smoke in the early stage of a fire before it becomes intense.