复杂场景下基于CNN的轻量火焰检测方法

CNN-Based Lightweight Flame Detection Method in Complex Scenes

已有的火灾检测方法往往依赖高性能的机器,在嵌入式端和移动端检测速度较慢、误检率较高,尤其是无法解决小尺度火焰漏检问题.针对上述问题,文中提出基于YOLO的火焰检测方法.使用深度可分离卷积改进火焰检测模型的网络结构,并使用多种数据增强技术与基于边框的损失函数以提高精度.通过参数调优,在保证检测准确率的情况下,实现在嵌入式移动系统上21 ms的实时火灾探测.实验表明,文中方法在火焰数据集上的精度和速度都有所提高.

The existing fire detection methods rely on high-performance machines,and therefore the speeds on the embedded terminals and the mobile ones are not satisfactory.For most of the detection methods,the speed is low and the false detection rate is high,especially for small-scale fires missed detection problems.To solve these problems,a fire detection method based on you only look once is proposed.Depthwise separable convolution is employed to improve its network structure.Multiple data augmentation and bounding box based loss function are utilized to achieve a higher accuracy.The real-time 21ms fire detection on embedded mobile system is realized through parameter tuning with the detection accuracy ensured.Experimental results show that the proposed method improves accuracy and speed on the fire dataset.