A heterogeneous smoke and flame visual detection method integrating visible and infrared images was proposed, in order to address the problems of low detection efficiency, poor accuracy, and consequent delays in fire disposal and misallocation of resources in the early stage of tunnel fires caused by lighting interference and structural environment. For infrared images, an OTSU threshold segmentation optimized by a grey wolf optimizer fused with differential evolution was applied, and the maximum entropy algorithm was combined to improve flame region segmentation accuracy. Considering the tunnel scene characteristics, YOLO11 was adapted, and the backbone network was optimized using a Swin-Transformer modified with a neural attention mechanism to enhance weak feature extraction. During feature fusion, a lightweight BiFPN was employed to adapt to multi-scale smoke and flame features, and an adaptive WIoU loss function was introduced to improve localization accuracy under fuzzy boundaries. At the decision layer, weighted fusion and conflict resolution were used to achieve dual-modal collaborative decision-making. Experimental results demonstrated that the proposed method achieved at least a 23.55% reduction in average localization error compared with single-modal detection. Compared with nine fusion algorithms such as evidence theory, the recall, F1, and mAP values of the proposed method reached 92.52%, 90.83%, and 93.47%, respectively, all exceeding those of the compared algorithms, while maintaining the smallest localization error fluctuation. The inference speed of the model increased by an average of 14.61%. The research results not only contribute to tunnel fire prevention and control but also effectively improve overall tunnel safety management and emergency response capability.