SCU-CGAN: Enhancing Fire Detection through Synthetic Fire Image Generation and Dataset Augmentation
This addresses the lack of sufficient fire datasets for household fire detection systems, which is an incremental improvement in dataset augmentation for a specific domain.
The paper tackled the problem of limited fire datasets for detection models by proposing SCU-CGAN to generate synthetic fire images, resulting in a 41.5% improvement in KID score over CycleGAN and a 56.5% increase in mAP@0.5:0.95 for YOLOv5 nano.
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 mAP@0.5:0.95 metric, which increased by 56.5%, highlighting the effectiveness of the proposed approach.