A Multi-Scale Object Detection Algorithm for Fires
Traditional fire detection relies on various sensors or some traditional algorithms.These methods are only applicable to specific scenarios,with poor robustness,low detection accuracy,inaccurate positioning,and a long detection time,which cannot meet real-time requirements.For the above problems,a YOLO V3-based multi-scale object detection algorithm is proposed to automatically extract flame features and realize multi-scale flame detection.Firstly,the YOLOV3 structure was improved to replace original YOLO V3 feature extraction network Darknet-53 with DenseNet and weaken the gradient extinction problem caused by network deepening,which can enhance the feature taking and improve the network learning of low-level features.Then,in order to eliminate the Gaussian distribution of pixels in the receptive field,the downsampling in DenseNet was changed to dilated convolution.Finally,the positioning mode of anchor box was optimized,and the loss function was modified according to new positioning mode,to make the network more accurate in locating targets.Experimental results show that the improved algorithm accuracy and recall rate are 90%and 85%respectively.The detection speed can reach 31 frames/s,which can meet the accu-racy and real-time requirements of fire detection.
Fire detectionComputer visionMulti-scale object detection
NETL
NSTL
万方数据
维普
