Indoor RGB-D image semantic segmentation method based on wavelet transform

The invention discloses an indoor RGB-D image semantic segmentation method based on wavelet transform. The method comprises the following steps: firstly, constructing a convolutional neural network based on wavelet transform; secondly, performing multi-scale fusion on image features of different frequencies of the original image at the joint of the encoder and the decoder through a wavelet transform and inverse transform combined module; and finally, carrying out multiple times of up-sampling on the features through a decoder network, carrying out double up-sampling on the features by each module of a decoder, better carrying out feature mapping through convolution and jump connection of an encoder, gradually recovering a high-resolution image, and outputting a semantic segmentation result. According to the method, high-frequency features which are not utilized by a conventional method are fully utilized, information such as contour details in image information is reserved, the indoor image semantic segmentat