Image fusion quality detection method and device
A quality inspection method and image fusion technology, applied in the field of image processing, can solve problems such as the failure to track people and the inability to accurately understand the situation of the person being photographed, and achieve a good positioning effect
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Embodiment 1
[0043] Embodiment 1 of the present application provides an image fusion quality detection method, such as figure 1 shown, including:
[0044] Step 110, searching for the image frame of the tracked person from each frame of the video image;
[0045] Since the embodiment of the present application has high real-time requirements for the tracked person, a deep convolutional neural network model with fast calculation speed is used for image judgment;
[0046] Among them, the image frame of the tracked person is searched from each frame of the video image, specifically: constructing a deep convolutional neural network model, starting from the input layer, and sequentially going through the convolution C 1 layer (output image size [256,256,8]), depthwise convolutional layer D 1 (output image size [128,128,16]), convolutional layer C 2 (output image size [64,64,32]), depth convolutional layer D 2 (output image size [32,32,64]), convolutional layer C 3 (output image size [16,16,1...
Embodiment 2
[0083] Embodiment 2 of the present application provides an image fusion quality detection device, such as Figure 5 shown, including:
[0084] The tracked person's image search module 510 is used to search for the tracked person's image frame from each frame of the video image;
[0085] The image fusion module 520 is used to perform image fusion on multiple tracked person images according to the wavelet transform image fusion method, the contour wavelet fusion method and the scale-invariant feature transform image fusion method;
[0086] The fusion image quality detection module 530 based on the standard deviation is used to calculate the standard deviation respectively according to the fusion results, and determine the image fusion quality according to the standard deviation.
[0087] Wherein, the tracked person image search module 510 is specifically used to construct a deep convolutional neural network model; starting from the input layer, sequentially passing through the ...
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