改进YOLOv3的火灾检测

Fire Detection Based on Improved YOLOv3

针对火灾检测中小目标检测率低、复杂场景下检测精度低和检测不及时等问题,提出了一种改进YOLOv3的火灾检测算法.首先,通过改进的K-means聚类算法重新获取更符合火焰和烟雾尺寸的anchor;其次在Darknet-53后添加空间金字塔池化,提升了网络的感受野进而增强了网络对小尺度目标的检测能力;然后通过CIoU改进损失函数,在计算坐标误差时考虑中心和宽高坐标两者的相关性,加快了损失函数的收敛;最后使用mosaic数据增强丰富了待检测物体的背景.在自制的数据集上训练并测试,实验结果表明:改进后的算法比YOLOv3火焰的AP从94%提升至98%,烟雾的AP从82%提升至94%,平均检测速度从31 fps提升至43 fps,相比Faster R-CNN、SDD等算法也有更高的mAP和更快的检测速度.因此,改进后的算法能够更有效地进行火灾预警.

Given the low detection rates of small targets, low detection accuracy in complex scenes, and delayed detection in fire detection, an improved you only look once v3(YOLOv3)-based fire detection algorithm is proposed. Firstly, an improved K-means clustering algorithm is used to retrieve anchors that are more in line with the sizes of the flames and smoke. Secondly, spatial pyramid pooling is added after the Darknet-53, which improves the network receptive field and enhances the detection ability of the network on small-scale targets. Thirdly, the loss function is improved through complete intersection over union(CIoU), and the convergence of the loss function is sped up by taking into consideration the correlations of the center with the width and height coordinates when calculating the coordinate error. Finally, mosaic data enhancement is employed to enrich the background of the object to be detected, and the improved algorithm is trained and tested on a self-made data set. The experimental results show that compared with the YOLOv3 algorithm, the improved algorithm improves the flame AP from 94% to 98%, increases the smoke AP from 82% to 94%, and promotes the average detection speed from 31 fps to 43 fps. Compared with the Faster R-CNN, SDD, and other algorithms, it also has a higher mAP and a faster detection speed. Therefore, the improved algorithm is more effective in fire warning.