The training of video fire detection models based on deep learning relies on a large number of positive and negative samples, namely, fire video and scenario video with other disturbances similar to fire. Due to the prohibition of ignition in lots of indoor occasions, the fire video samples in the scene are insufficient. In this paper, a method based on generative adversarial network is proposed to generate flame images which are then migrated into specified scenes, thus increasing fire video samples in those restricted situations. Flame kernel is pre-implanted into the specified scene to keep its characteristics intact. The flame and scene are blended together by adding styling information such as blurry edge and ground reflection. This method overcomes background distortion which is caused by existing multimodal image translation on as a result of information loss and is able to guarantee the diversity of flames in specified scenes and produce perceptually realistic results. Compared with other multimodal image-to-image translation schemes, the FID and LPIPS values of images generated by our method are the highest, reaches 118.4 and 0.1322 respectively. In addition, Unet and the SA-Unet, in which a self-attention mechanism is involved, are used as fire segmenting networks to evaluate the enhancement of the augmented data on improving the accuracy of segmented network. Their F1-scores reaches 0.8905 and 0.9082 respectively after Unet and SA-Unet are trained with GAN-based augmented dataset generated by our model. The F1-scores are second only to 0.9259 and 0.9291 which are obtained when Unet and SA-Unet are trained with real picture serving as augmented dataset.

基于深度学习的视频火灾检测模型的训练依赖于大量的正负样本，即火灾视频和其他类似火灾干扰的场景视频。由于室内很多场合禁止点火，现场火灾视频样本不足。在本文中，提出了一种基于生成对抗网络的方法来生成火焰图像，然后将其迁移到指定场景中，从而在这些受限情况下增加火灾视频样本。火焰内核预先植入到指定场景中，以保持其特性完整。通过添加模糊边缘和地面反射等样式信息，将火焰和场景混合在一起。该方法克服了现有多模态图像平移由于信息丢失而导致的背景失真，能够保证指定场景中火焰的多样性并产生感知逼真的结果。与其他多模态图像到图像转换方案相比，我们的方法生成的图像的 FID 和 LPIPS 值最高，分别达到 118.4 和 0.1322。此外，Unet和SA-Unet，其中涉及自注意力机制，被用作火分割网络来评估增强数据对提高分割网络准确性的增强。在使用我们的模型生成的基于 GAN 的增强数据集训练 Unet 和 SA-Unet 后，它们的 F1 分数分别达到 0.8905 和 0.9082。F1 分数仅次于 0.9259 和 0。