Zhao Xuanyi, Yu Minrui, Xu Jiaxing, Wu Peng, Yuan Haotian
School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
School of Urban Construction, Yangtze University, Jingzhou 434023, China.
Sensors (Basel). 2025 Sep 5;25(17):5528. doi: 10.3390/s25175528.
With the escalating frequency of urban and forest fires driven by climate change, the development of intelligent and robust fire detection systems has become imperative for ensuring public safety and ecological protection. This paper presents a comprehensive multi-module fire detection framework based on visual computing, encompassing image enhancement and lightweight object detection. To address data scarcity and to enhance generalization, a projected generative adversarial network (Projected GAN) is employed to synthesize diverse and realistic fire scenarios under varying environmental conditions. For the detection module, an improved YOLOv8n architecture is proposed by integrating BiFormer Attention, Agent Attention, and CCC (Compact Channel Compression) modules, which collectively enhance detection accuracy and robustness under low visibility and dynamic disturbance conditions. Extensive experiments on both synthetic and real-world fire datasets demonstrated notable improvements in image restoration quality (achieving a PSNR up to 34.67 dB and an SSIM up to 0.968) and detection performance (mAP reaching 0.858), significantly outperforming the baseline. The proposed system offers a reliable and deployable solution for real-time fire monitoring and early warning in complex visual environments.
随着气候变化导致城市和森林火灾发生频率不断上升，开发智能且强大的火灾探测系统对于确保公共安全和生态保护变得至关重要。本文提出了一种基于视觉计算的综合多模块火灾探测框架，涵盖图像增强和轻量级目标检测。为了解决数据稀缺问题并提高泛化能力，采用了投影生成对抗网络（Projected GAN）来合成不同环境条件下多样且逼真的火灾场景。对于检测模块，通过集成BiFormer注意力、Agent注意力和CCC（紧凑通道压缩）模块，提出了一种改进的YOLOv8n架构，这些模块共同提高了在低能见度和动态干扰条件下的检测精度和鲁棒性。在合成和真实世界火灾数据集上进行的大量实验表明，图像恢复质量（PSNR高达34.67 dB，SSIM高达0.968）和检测性能（mAP达到0.858）有显著提高，明显优于基线。所提出的系统为复杂视觉环境中的实时火灾监测和预警提供了可靠且可部署的解决方案。
