Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM
Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM
复制标题
基于改进果蝇优化SVM的火灾检测方法
DOI：
10.32604/cmc.2020.06258
复制
发表时间：
2020-01-01
影响因子：
3.1
通讯作者：
Assefa, Biruk
中科院分区：
文献类型：
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作者：
Bi, Fangming;Fu, Xuanyi;Assefa, Biruk
针对传统火灾探测方法在大空间建筑物火灾探测中容易出现误报和漏报的缺陷，提出了一种基于视频图像的火灾识别探测方法。该算法首先采用混合高斯背景建模方法和RGB颜色模型对视频图像进行火灾预判断，消除了大部分非火灾干扰。其次，对传统的区域生长算法进行了改进，有效地提高了火灾图像的分割效果。然后，基于分割后的图像，在疑似火灾火焰区域内进一步分析和提取火灾火焰的动态和静态特征。最后，对提取的火灾火焰图像进行动态特征融合，并采用改进的果蝇优化支持向量机进行分类，得到识别结果。本文提出的基于视频的火灾检测方法大大提高了火灾检测的准确性，适用于大空间场景下的火灾检测与识别。
Aiming at the defects of the traditional fire detection methods, which are caused by false positives and false negatives in large space buildings, a fire identification detection method based on video images is proposed. The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image, which can eliminate most non-fire interferences. Secondly, the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved. Then, based on the segmented image, the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame. Finally, the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine, and the recognition results were obtained. The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.