激光与光电子学进展, 2019, 56 (16): 161012, 网络出版: 2019-08-05   

基于图像的火焰检测算法 下载: 1163次

Flame Detection Algorithm Based on Image Processing Technology
作者单位
1 江南大学物联网工程学院, 江苏 无锡 214122
2 无锡职业技术学院, 江苏 无锡 214122
摘要
在传统的火焰检测算法中,火焰前景提取容易出现火焰轮廓不完整和抗干扰性较差的情况。为此,融合红/绿/蓝(RGB)、色调/饱和度/亮度(HSI)和最大类间方差法(Otsu)提出一种新的火焰前景提取算法,利用双颜色空间融合的算法能够提取较完整的火焰轮廓,使火焰轮廓所受干扰影响程度尽量小。获得前景图像后用灰度共生矩阵提取纹理特征,在YCbCr颜色空间中提取颜色特征,用于最终的火焰判断。同时提出一种改进的概率神经网络(PNN),将传统PNN中单一固定值的平滑因子改进为多变量参数,用条件期望最大化(ECM)算法对PNN中平滑因子进行参数优化,再将提取的特征输入改进后的PNN中训练测试。仿真结果表明,该算法具有良好的抗干扰能力,能够提高对火焰识别的精度。
Abstract
The traditional flame detection algorithm often achieves incomplete contour and poor anti-interference performance in the process of flame foreground extraction. This paper proposes a new flame foreground extraction algorithm, which combines RGB, HSI, and Ostu (maximum between-cluster variance method). The developed algorithm can extract flame contour completely and eliminate the smallest possible interference. Then, static features such as textures and colors in YCbCr are extracted by using a co-occurrence matrix and used for final flame judgment. Finally, an improved probabilistic neural network (PNN) method is developed to adjust the traditional smoothing factor from a single fixed value to a parameter that contains multi-variables, after which the expectation/conditional maximization (ECM) algorithm is used to find the optimal parameters. The extracted features are input in the advanced PNN and used for the training test. Simulation results show that the proposed algorithm can improve the accuracy of flame identification with good anti-interference performance.

谭勇, 谢林柏, 冯宏伟, 彭力, 张正道. 基于图像的火焰检测算法[J]. 激光与光电子学进展, 2019, 56(16): 161012. Yong Tan, Linbo Xie, Hongwei Feng, Li Peng, Zhengdao Zhang. Flame Detection Algorithm Based on Image Processing Technology[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161012.

本文已被 3 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!