Road scene segmentation method based on residual network and expanded convolution

Abstract

The invention discloses a road scene segmentation method based on a residual network and expanded convolution. The method comprises: a convolutional neural network being constructed in a training stage, and a hidden layer of the convolutional neural network being composed of ten Respondial blocks which are arranged in sequence; inputting each original road scene image in the training set into a convolutional neural network for training to obtain 12 semantic segmentation prediction images corresponding to each original road scene image; calculating a loss function value between a set formed by12 semantic segmentation prediction images corresponding to each original road scene image and a set formed by 12 independent thermal coding images processed by a corresponding real semantic segmentation image to obtain an optimal weight vector of the convolutional neural network classification training model. In the test stage, prediction is carried out by utilizing the optimal weight vector of the convolutional neural network classification training model, and a predicted semantic segmentation image corresponding to the road scene image to be subjected to semantic segmentation is obtained. The method has the advantages of low calculation complexity, high segmentation efficiency, high segmentation precision and good robustness.

Description

technical field [0001] The present invention relates to a deep learning semantic segmentation technology, in particular to a road scene segmentation method based on residual network and dilated convolution. 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 image classification, localization, and scene understanding. With the proliferation of tasks such as augmented reality and ...

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. However, the original design of FCN requires an output that is consistent with the input size, so FCN does upsampling, but upsampling cannot retrieve

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