semantic segmentation
در نشریات گروه فناوری اطلاعات-
Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, and disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replaces the fully connected layers in common classification networks by convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. This paper semantic segmentation methods based on deep learning. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed.
Keywords: Semantic segmentation, Convolutional neural network, Deep neural network, High-resolution image processing -
Recent researches on pixel-wise semantic segmentation, use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predict pixel tags, but the results seem to be undesirable. The reason for these unacceptable results is mainly due to the existence of max pooling operators, which reduces the resolution of the feature maps. In this paper, we present a convolutional neural network composed of encoder-decoder segments based on successful SegNet network. The encoder section has a depth of 2, which in the first part has 5 convolutional layers, in which each layer has 64 filters with dimensions of 3×3. In the decoding section, the dimensions of the decoding filters are adjusted according to the convolutions used at each step of the encoding. So, at each step, 64 filters with the size of 3×3 are used for coding, the weights of these filters are adjusted by network training and adapted to the educational data. Due to having the low depth of 2, and the low number of parameters in the proposed network, the speed and the accuracy improve in comparison with the popular networks such as SegNet and DeepLab. For the CamVid dataset, after a total of 60,000 iterations, we attain the 91% for global accuracy, which indicates improvements in the efficiency of the proposed methodKeywords: semantic segmentation, convolutional neural networks, encoder –decoder, pixelwise semantic interpretation
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