用合成图像增强火情检测数据集，显著提升识别准确率。
SCU-CGAN: Enhancing Fire Detection through Synthetic Fire Image Generation and Dataset Augmentation
- 用改进的CGAN模型从非火图像生成逼真火焰图
- 生成图像质量比CycleGAN高41.5%（KID评分）
- 无需改动模型结构，检测精度提升56.5%（[email protected]:0.95）
火灾长期与人类生活相关，常引发严重灾害。早期检测至关重要，随着家庭物联网技术发展，家用火灾检测系统应运而生。然而，缺乏充足火情数据集限制了检测模型性能。本文提出SCU-CGAN模型，融合U-Net、CBAM和额外判别器，将非火图像转化为逼真火焰图像。评估显示，该模型生成图像质量优于现有方法：相比CycleGAN，KID得分提升41.5%。此外，实验表明，使用增强数据集可显著提升检测模型精度，无需修改模型结构。以YOLOv5 nano为例，[email protected]:0.95指标提升56.5%，验证了该方法的有效性。
原文摘要 · Abstract (English)
Fire has long been linked to human life, causing severe disasters and losses. Early detection is crucial, and with the rise of home IoT technologies, household fire detection systems have emerged. However, the lack of sufficient fire datasets limits the performance of detection models. We propose the SCU-CGAN model, which integrates U-Net, CBAM, and an additional discriminator to generate realistic fire images from nonfire images. We evaluate the image quality and confirm that SCU-CGAN outperforms existing models. Specifically, SCU-CGAN achieved a 41.5% improvement in KID score compared to CycleGAN, demonstrating the superior quality of the generated fire images. Furthermore, experiments demonstrate that the augmented dataset significantly improves the accuracy of fire detection models without altering their structure. For the YOLOv5 nano model, the most notable improvement was observed in the [email protected]:0.95 metric, which increased by 56.5%, highlighting the effectiveness of the proposed approach.
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