Sun Haoliang, Hao Xiaojian, Wang Jia, Pan Baowu, Pei Pan, Tai Bin, Zhao Yangcan, Feng Shenxiang
Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China.
School of Instrument and Electronics, North University of China, Taiyuan 030051, China.
ACS Omega. 2022 Jul 22;7(30):26680-26686. doi: 10.1021/acsomega.2c02858. eCollection 2022 Aug 2.
In this study, an improved flame edge detector based on convolutional neural network (CNN) was proposed. The proposed method can generate edge graphs and extract edge graphs relatively effectively. Our network architecture was based on VGG16 primarily, the last two max-pooling operators and all full connection layers of the VGG16 network were deleted, and the rest was taken as the basic network. The images output by the five convolution layers were upsampled to the size of the input images and finally fused to the edge image. Error calculation and back propagation of the fusion image and label image are carried out to form a weakly supervised model. Using the open datasets BSDS500 to train the network, the ODS F-measure can reach 0.810. Various experiments were carried out on different flame and fire images, including butane-air flame, oxygen-ethanol flame, energetic material flame, and oxygen-acetylene premixed jet flame, and the infrared thermogram was also verified by our method. The results demonstrate the effectiveness and robustness of the proposed algorithm.
在本研究中，提出了一种基于卷积神经网络（CNN）的改进型火焰边缘检测器。所提方法能够相对有效地生成边缘图并提取边缘图。我们的网络架构主要基于VGG16，删除了VGG16网络的最后两个最大池化算子和所有全连接层，其余部分作为基础网络。将五个卷积层输出的图像上采样到输入图像的大小，最后融合成边缘图像。对融合图像和标签图像进行误差计算和反向传播，以形成弱监督模型。使用公开数据集BSDS500训练网络，ODS F-measure可达0.810。对不同的火焰和火灾图像进行了各种实验，包括丁烷-空气火焰、氧气-乙醇火焰、含能材料火焰和氧气-乙炔预混射流火焰，并且我们的方法也对红外热成像图进行了验证。结果证明了所提算法的有效性和鲁棒性。
