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.