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Vanilla Transformers focus on semantic relevance between mid- to high-levelfeatures and are not good at extracting smoke features as they overlook subtlechanges in low-level features like color, transparency, and texture which areessential for smoke recognition. To address this, we propose the Cross ContrastPatch Embedding (CCPE) module based on the Swin Transformer. This moduleleverages multi-scale spatial contrast information in both vertical andhorizontal directions to enhance the network's discrimination of underlyingdetails. By combining Cross Contrast with Transformer, we exploit theadvantages of Transformer in global receptive field and context modeling whilecompensating for its inability to capture very low-level details, resulting ina more powerful backbone network tailored for smoke recognition tasks.Additionally, we introduce the Separable Negative Sampling Mechanism (SNSM) toaddress supervision signal confusion during training and release theSKLFS-WildFire Test dataset, the largest real-world wildfire testset to date,for systematic evaluation. Extensive testing and evaluation on the benchmarkdataset FIgLib and the SKLFS-WildFire Test dataset show significant performanceimprovements of the proposed method over baseline detection models. The codeand data are available at github.com/WCUSTC/CCPE.

Wildfire Smoke Detection System: Model Architecture, Training Mechanism, and Dataset