Abstract
•An attention mechanism is proposed to improve the flow of information between the swin transformer block.•A convolution method is proposed and used to eliminate redundant features, as a part of FPN.•A loss function is proposed to enhance the feature extraction effect of the smoke edge.•Forest Fire Smoke Complex Background Detection Dataset will be open-sourced with the release of the paper.
Smoke is an early manifestation of forest fire. Accurate identification of smoke from forest fires is crucial for the prevention and control of forest fires, which helps protect the ecological environment and the safety of people. The texture features of smoke are complex and prone to detection omissions. The forest environment is complex, and smoke-like objects in the forest often interfere with smoke recognition. The concentration of smoke at the edge is thin, which easily leads to edge omission. In response to these problems, we propose a high-precision edge focused forest fire smoke detection network. To begin, in response to the problem of detection omission, we present a Swin multidimensional window extractor (SMWE) that enhances information exchange between windows in both horizontal and vertical dimensions to extract global texture features from images with smoke. Then, the guillotine feature pyramid network (GFPN) is suggested, along with a new guillotine convolution method for reducing redundant feature information from a feature fusion perspective, thereby improving the anti-interference ability of the model. Finally, taking into account the thinness and irregularity of the smoke near the borders, a contour adaptive loss function is suggested to minimize the boundary blur caused by down-sampling the feature map in the network. The experimental and application results show that SMWE-GFPNNet accomplishes 80.92 % of the mAP, 90.01 % of the mAP50, and 83.38 % of the mAP75 on the Forest Fire Smoke Complex Background Detection Dataset. Excellent in anti-interference ability and accuracy.