改进的混合高斯与YOLOv2融合烟雾检测算法

提出一种融合了改进的混合高斯和YOLOv2的烟雾检测算法。首先,针对烟雾的早期特征对混合高斯算法进行改进,有效框定动态目标感兴趣区域,提取出烟雾前景;在此基础上将烟雾检测转换为回归问题,利用端对端目标检测算法YOLOv2训练烟雾数据集,进行二次检测和筛选,最终框定出烟雾发生区域的具体位置和范围,满足对不同场景火灾烟雾的有效检测。实验结果表明,融合算法改善了烟雾区域的检测效果,提高准确性并有效降低烟雾误检率。

Smoke Detection Algorithm Combined with Improved Gaussian Mixture and YOLOv2

A smoke detection algorithm based on improved Gaussian mixture and YOLOv2 is proposed to detect fire timely and effectively. First of all, according to the characteristics of the early smoke makes the improvement to the Gaussian mixture, effectively framing the dynamic target region of interest, to extract the smoke foreground based on smoke detection; converted to regression problems, using end-to-end target detection algorithm YOLOv2 smoke training data set, second detection and screening, the final box set and the specific location the scope of the smoke area, meet the effective detection of the different scenes of smoke. The experimental results show that the fusion algorithm improves the detection effect of smoke area, improves the accuracy and effectively reduces the false detection rate to the smoke.