MMFNet: Forest Fire Smoke Detection Using Multiscale Convergence Coordinated Pyramid Network With Mixed Attention and Fast-Robust NMS

There is a problem in the field of early automatic detection of forest fire smoke that due to low concentration or tiny size, some smoke is difficult to capture. This article proposes a multiscale convergence coordinated pyramid network (MCCPN) with mixed attention and Fast-robust NMS (MMFNet) for the fast detection of forest fire smoke. First, an MCCPN is designed, which combines a dual-attention feature pyramid network and a coordinated convergence module. It improves the detection rate of targets of different sizes. Second, a mixed attention module is designed to focus more on the smoke in the image and enhance the extraction of horizontal and vertical features of smoke. Then, a Fast-robust nonmaximum suppression is proposed to accelerate the convergence of bounding boxes and increase the accuracy of the prediction box. Finally, a forest fire detection system of the Internet of Things using MMFNet is built. The experimental results show that our method achieves 80.72% AP, 88.52% AP50, 83.45% AP75, 46.88% AR, and 154 FPS, which is superior to the state-of-art forest fire smoke detection methods.