Abstract
With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.
Highlights
Convolutional Neural Network (CNN) was first proposed by Hubel and Wiesel in the 1960 s [1]
CNN is based on the structure of the shared convolution kernel, which makes it have great advantages in processing high-dimensional images of actual size. It realizes the encapsulation of feature extraction. e user does not need to care about the specific features trained, just that they are trained well, the weight is enough, the classification effect is good, and the accuracy is high. e disadvantage is that it requires a large amount of sample data, a large amount of calculation, and adjusting parameters
Method of semantic image segmentation based on separable convolutional neural network
Summary
Convolutional Neural Network (CNN) was first proposed by Hubel and Wiesel in the 1960 s [1]. Cho SI proposed a new image semantic segmentation method based on CNN, which first uses separable convolution and gradient to reduce computational complexity and improve image segmentation and denoising performance [4]. E innovations of this article are as follows: (1) proposing a separable CNN algorithm model; (2) constructing a separable CNN codec structure; (3) designing a separable convolution image semantic segmentation process of neural network. 2. Semantic Image Segmentation Research Method Based on Separable Convolutional Neural Network Codec. Method of semantic image segmentation based on separable convolutional neural network. E experimental part of this article proposes that the above steps are used for the codec based on a separable CNN for semantic image segmentation research experiments.
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