基于改进YOLOv3的火灾检测与识别

现阶段火灾频发，需要自动进行火灾的检测与识别，虽然存在温度、烟雾传感器等火灾检测手段，但是检测实时性得不到保证.为了解决这一问题，提出了基于改进YOLOv3的火灾检测与识别的方法.首先构建一个多场景大规模火灾目标检测数据库，对火焰和烟雾区域进行类别和位置的标注，并针对YOLOv3小目标识别性能不足的问题进行了改进.结合深度网络的特征提取能力，将火灾检测与识别形式化为多分类识别和坐标回归问题，得到了不同场景下火焰和烟雾两种特征的检测识别模型.实验表明，本文提出的改进YOLOv3算法对不同拍摄角度、不同光照条件下的火焰和烟雾检测都能得到理想的效果，同时在检测速度上也满足了实时检测的需求.

Fire Detection and Identification Based on Improved YOLOv3

At the present stage, fires occur frequently, and fire detection and identification are required automatically. Although there are fire detection methods such as temperature and smoke sensors, the real-time detection is not guaranteed. To solve this problem, a method based on improved YOLOv3 fire detection and identification is proposed. Firstly, a multi-scenario large-scale fire target detection database was constructed to mark the categories and locations of the flame and smoke areas, and the problem of insufficient performance of YOLOv3 small target recognition was improved. Combined with the feature extraction ability of deep network, the fire detection and recognition were formalized into multi-class recognition and coordinate regression problems. The detection and recognition models of flame and smoke were obtained under different scenarios. Experiments show that the improved YOLOv3 algorithm proposed in this study can achieve ideal results for flame and smoke detection under different shooting angles and different illumination conditions, and also meets the real-time detection requirements in terms of detection speed.