Abstract
Semantic segmentation plays a very important role in computer vision. It can be used in many real-world applications, such as virtual reality and augmented reality, robotics and autopilot technology. The existing models have large amount of network parameters and high complexity, and can not fully extract the context information of the image. In order to solve the problem of high complexity and poor real-time performance of DenseNet, the backbone network of DenseAspp model, this paper proposes an image semantic segmentation method based on Shufflenetv2. The lightweight convolutional neural network Shufflenet-v2 is used to replace DenseNet as the backbone network of the segmentation model to extract features, which effectively reduces the amount of parameters and calculation of the model and improves the real-time performance of the segmentation algorithm. SkNet, a selective convolution kernel mechanism, enables each neuron to adaptively select the size of receptive field according to the multi-scale information of input features, thus improving the segmentation accuracy.
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Zhong, G., Zhao, H., Liu, G. (2022). Lightweight Semantic Segmentation Convolutional Neural Network Based on SKNet. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_15
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DOI: https://doi.org/10.1007/978-981-16-6554-7_15
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