改进Yolo-v3的视频图像火焰实时检测算法

A Real-Time Video Flame Detection Algorithm Based on Improved Yolo-v3

为解决现有视频图像火焰检测方法精度低、速度慢的问题,提出了改进Yolo-v3的视频火焰实时检测算法。首先,在特征提取阶段,通过进一步融合多尺度特征提高网络对图像浅层信息的学习能力,以实现小火焰区域的精准识别;其次,在目标检测阶段,利用改进的K-means聚类算法优化多尺度先验框以适应火焰不同尺寸;最后,在改进Yolo-v3的视频火焰检测之后,利用火焰特有的闪烁特征对检测结果中的误检帧进行排除,进一步提高检测精度。从精度和速度两个方面对视频火焰进行检测,并与近年来先进的火焰检测算法对比,结果表明,该方法准确率均值可达到98.5%,误检率低至2.3%,平均检测速率为52帧/s,在精度和速度方面皆有更好的表现。

Objectives:In order to solve the problems of the low accuracy and the slow speed of the existing video image flame detection methods,we propose a real-time video flame detection algorithm based on improved Yolo-v3 to achieve real-time and efficient detection of flames in the video.Methods:Firstly,in the feature extraction stage,the multi-scale detection network is improved.We add a new-scale feature and then improved the network s ability to learn the shallow information of the images by further integrating multi-scale features.Using this method,the accurate identification of small flame is achieved.Secondly,in the target detection stage,we use the improved K-means clustering algorithm to optimize the multi-scale prior frames,and make them adapt to the changing posture and shape of the flame.Finally,after detecting video flames based on improved Yolo-v3,we use the unique flicker characteristics of the flame to check the video again,and eliminate the false detection frame in the detection result.And in this method the detection accuracy is further improved.Results:In order to prove the effectiveness of our method,the video flames are detected from both accuracy and speed,and the results are compared with the advanced flame detection methods in recent years.The results show that the average accuracy rate of our method can reach 98.5%,the false detection rate is as low as 2.3%,and the average detection rate is 52 frames/s,so our method has better performance in terms of accuracy and speed.Conclusions:The effectiveness of this method is proved through multiple sets of experiments.Comparing with the existing flame detection methods,our method can be more effectively applied to video flame detection.