激光与光电子学进展, 2021, 58 (4): 0410023, 网络出版: 2021-02-24
TDFF:一种强鲁棒性的烟雾图像检测算法 下载: 823次
TDFF: Strong Robust Algorithm for Smoke Image Detection
图像处理 烟雾检测 局部二值模式 Gabor 特征融合 image processing smoke detection local binary patterns Gabor feature fusion
摘要
烟雾图像检测是及早发现火灾的一种重要手段。针对传统LBP(Local Binary Patterns) 特征与Gabor特征的融合算法存在鲁棒性和检测率低的问题,提出一种TDFF(Triple Multi Feature Local Binary Patterns and Derivative Gabor Feature Fusion)的烟雾检测算法。采用T-MFLBP(Triple Multi Feature Local Binary Patterns)算法分别对像素间不同灰度差值以及非均匀模式中特殊位置的像素进行编码计算,可以捕捉更清晰的纹理特征;然后利用高斯核函数的一阶偏导数提取Gabor特征,从而优化提取图像边缘灰度信息的性能;最后对融合后的特征进行训练,可以提高最终分类的准确性。实验结果表明,TDFF算法具有较强的鲁棒性,烟雾图像的检测率也显著优于未改进的传统算法。
Abstract
Smoke image detection is an important means for early detection of fires. Aiming at the problems of low robustness and low detection rate of traditional LBP (Local Binary Patterns) feature and Gabor feature fusion algorithms, a TDFF (Triple Multi Feature Local Binary Patterns and Derivative Gabor Feature Fusion) smoke detection algorithm is proposed. First, the T-MFLBP(Triple Multi Feature Local Binary Patterns) algorithm is used to encode and calculate the different grayscale differences between pixels and the pixels at special positions in the non-uniform mode, which can capture clearer texture features. Second, the first-order partial derivative of the Gaussian kernel function is used to extract Gabor features, so as to optimize the performance of extracting image edge gray information. Finally, the fusion features can be trained to improve the accuracy of the final classification. The experimental results show that the TDFF algorithm has strong robustness, and the detection rate of smoke images is also significantly better than the unimproved traditional algorithm.
王韦刚, 王炳蔚, 张云伟. TDFF:一种强鲁棒性的烟雾图像检测算法[J]. 激光与光电子学进展, 2021, 58(4): 0410023. Weigang Wang, Bingwei Wang, Yunwei Zhang. TDFF: Strong Robust Algorithm for Smoke Image Detection[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410023.