SSmokeDet: A novel network dedicated to small-scale smoke detection

Small smoke detection is essential for the warning of early and distant smoke. However, small-scale smoke occupies few pixels and only provides limited semantic information, causing a considerable challenge for its detection. To this end, we propose a novel network dedicated to small-scale smoke detection (SSmokeDet). Firstly, we put forward a small-net (SNet) backbone to control the receptive field of the model, which facilitates a better observation of the small smoke. Secondly, combined with a residual connection, a multiple spatial pyramid pooling (MultiSPP) is designed to compensate for the lack of small smoke information on the high level by contextual information reinforcement. Lastly, a self-cooperation head (SCHead) is devised for cross-layer communication after refining branching features at different scales. Moreover, an anchor-free mechanism is employed to break the size limitation of predefined anchor boxes and decode the smoke location information directly for the small-scale smoke detection task. Extensive experiments are conducted on both self-made and synthetic databases with various scenes, and the results demonstrate that our SSmokeDet is superior to the state-of-the-art methods. Compared with the baseline, the accuracy of small-scale smoke is effectively improved by 10.2%, and the average precision is increased by 4.9%.