Multi-scale image semantic segmentation method
A semantic segmentation and multi-scale technology, applied in the field of computer vision, can solve the problems of loss of details of segmentation results, low utilization efficiency of receptive field features, and insufficient robustness of segmentation, etc., to reduce the amount of calculation and the number of parameters, and reduce the calculation volume and number of parameters, the effect of increasing utilization
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[0047] Such as figure 1 As shown, a multi-scale image semantic segmentation method includes the following steps:
[0048] S1. Obtain an image to be segmented and a corresponding label, the image to be segmented is a three-channel color image, and the label is a category label corresponding to each pixel position;
[0049] S2. Construct a fully convolutional deep neural network, such as Figure 4 As shown, the full convolution deep neural network includes a convolution module, a hole convolution module, a pyramid pooling module, a 1×1×depth convolution layer, and a deconvolution structure; the hole convolution module includes several groups A multi-scale atrous convolution structure, the multi-scale atrous convolution structure is provided with atrous convolution kernels of different expansion rates, and extracts information of low, medium, and high-resolution targets from the feature image; step S2 specifically includes the following steps:
[0050] S21. The fully convoluti...
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