Streetscape image segmentation method integrating network and dual-channel attention mechanism

Abstract

The invention discloses a street view image segmentation method integrating a network and a dual-channel attention mechanism. The method comprises a training stage and a test stage. In the training stage, an image segmentation convolutional neural network model based on a high-resolution fusion network and a dual-channel attention mechanism is constructed, and the model is trained, wherein the model comprises an input layer, a hidden layer and an output layer, and the hidden layer comprises four expansion convolution blocks stacked in a dense sampling mode, a high-resolution fusion network anda dual-channel attention mechanism; in the test stage, the streetscape image to be segmented is predicted to obtain a predicted semantic segmentation image, i.e., image semantic segmentation based ona high-resolution fusion network and a dual-channel attention mechanism is realized. The method is high in segmentation precision and better in robustness.

Description

technical field [0001] The invention belongs to the technical field of image semantic segmentation, and relates to a semantic segmentation technology based on deep learning, in particular to a semantic segmentation method for street view images based on a high-resolution fusion network and a dual-channel attention mechanism. Background technique [0002] Deep learning is a branch of artificial neural network, and artificial neural network with deep network structure is the earliest network model of deep learning. Initially, the application of deep learning was mainly in the field of image and speech. Since 2006, deep learning has continued to heat up in academia. Deep learning and neural networks have been widely used in semantic segmentation, computer vision, speech recognition, and tracking. Its high efficiency also makes it suitable for real-time applications, etc. It has huge potential in all aspects. [0003] Convolutional neural networks have achieved success in imag...

AI Technical Summary

Problems solved by technology

However, one of the shortcomings of FCN is that due to the existence of the pooling layer, the size of the response tensor (length and width) is getting smaller and smaller, and the original design of FCN requires an output that is consistent with the input size, so FCN does However, upsampling cannot retrieve all the lost information losslessly; the convolutional neural network SegNet is a network model built on the basis of FCN, but it does not control the problem of information loss well.

Therefore, these methods affect the accuracy of image semantic segmentation due to information loss, and the robustness of the method is also low.

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