一种基于YOLOv8n的增强方法,用于复杂场景中的火灾检测
Xuanyi Zhao1, Minrui Yu2, Jiaxing Xu2
1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种使用视觉计算的先进火灾检测系统,以提高安全性. 该框架在具有挑战性的条件下提高了火灾检测准确性和稳定性,提供了可靠的早期预警解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 环境监测 环境监测
背景情况:
- 气候变化正在增加火灾频率,需要先进的检测系统.
- 现有系统面临着低可见度和动态干扰的困难.
- 公共安全和生态保护要求强大的火灾检测.
研究的目的:
- 开发一个全面的多模块火灾检测框架.
- 在复杂的环境中提高火灾检测准确性和稳定性.
- 使用生成对抗网络解决数据稀缺问题.
主要方法:
- 使用视觉计算进行图像增强和轻量级物体检测.
- 预计生成对抗网络 (预计GAN) 用于数据合成.
- 改进了YOLOv8n架构,包括BiFormer注意力,代理注意力和紧通道压缩 (CCC) 模块.
主要成果:
- 实现了高质量的图像恢复 (PSNR高达34.67dB,SSIM高达0.968).
- 达到显著的检测性能 (mAP为0.858).
- 在合成和现实世界的数据上,与基线方法相比,表现出优越的性能.
结论:
- 拟议的框架为实时火灾监测提供了可靠和可部署的解决方案.
- 该系统有效地处理低可见度和动态干扰.
- 这一进步有助于改善火灾事件的预警系统.
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