摘要
针对消防无人机图像识别的挑战,文章提出一种基于Python的消防无人机图像识别方法。首先,设计多分支ResNet架构(MB-FireNet)以增强火灾特征提取能力;其次,开发基于物理模型驱动的自适应图像增强框架,有效应对烟雾干扰问题;最后,构建轻量化目标检测模型FireLiteNet,以适配无人机边缘计算平台。通过Python生态系统的优化实现,该方法显著提升了复杂环境下的火源检测精度与系统实时性能,为无人机辅助消防救援提供了有效的技术解决方案。
In response to the challenges of fire drone image recognition,this article proposes a Python based method for fire drone image recognition.Firstly,design a multi branch ResNet architecture(MB FineNet)to enhance the ability of fire feature extraction.Secondly,develop an adaptive image enhancement framework driven by physical models to effectively address smoke interference issues.Finally,a lightweight target detection model FireLiteNet is constructed to adapt to the UAV edge computing platform.By optimizing the Python ecosystem,this method significantly improves the accuracy of fire source detection and real-time system performance in complex environments,providing an effective technical solution for unmanned aerial vehicle assisted firefighting and rescue.
作者
严岚
YAN Lan(Shanghai Shibei Vocational High School,Shanghai 200071,China)
