改进YOLOV3的火灾检测方法  

Improved YOLOV3 Fire Detection Method

作　　者：罗小权 潘善亮[1] LUO Xiaoquan;PAN Shanliang(School of Information Science and Engineering,Ningbo University,Ningbo,Zhejiang 315211,China)

机构地区：[1]宁波大学信息科学与工程学院,浙江宁波315211

出　　处：《计算机工程与应用》2020年第17期187-196,共10页Computer Engineering and Applications

基　　金：浙江省公益性技术应用研究计划项目(No.2017C33001)。

摘　　要：针对传统火灾探测器检测范围有限,传统火灾检测算法精度不高、检测时间长等问题,提出一种基于改进YOLOV3的火灾检测方法YOLOV3-IMP。在YOLOV3网络结构上进行改进,包含对特征提取网络改进和多尺度检测改进,提高对浅层特征的学习能力;通过改进的K-means聚类算法生成出初始先验框;通过改进的损失函数提高对小火灾区域的检测能力;在输出火灾检测图像之前采用Softer-NMS算法,提高对重叠区域的检测能力。实验结果表明,改进的算法准确率和召回率为91.6%,83.2%,mAP高达84.5%,检测速度可达0.28 s,可以满足火灾检测的实时性和准确性,能够实现有效的火灾检测。Aiming at the problems of traditional fire detectors with limited detection range,low accuracy of traditional fire detection algorithms and long detection time,a fire detection method YOLOV3-IMP based on improved YOLOV3 is proposed.YOLOV3 network structure is improved to enhance the ability to learn shallow features,including improvements to feature extraction networks and multi-scale detection.Initial prior frames are generated through an improved K-means clustering algorithm.Detection capabilities are improved for small fire areas through an improved loss function.Softer-NMS algorithm is adopted before outputting fire detection images to improve detection ability for overlapping areas.Experimental results show that the improved algorithm’s accuracy and recall rate is 91.6%,83.2%,mAP is as high as 84.5%,and the detection speed is up to 0.28 s.It can meet the real-time and accuracy of fire detection,which can achieve effective fire detection.

关 键 词：火灾检测 YOLOV3-IMP 多尺度检测 Softer-NMS 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]