Accelerating SegNet-Based Semantic Segmentation Using a Model Post-Pruning Strategy

Abstract
Accelerating deep convolutional networks has recently attracted a great deal of attention due to the demand of real-time applications. SegNet is a typical deep convolution network in the field of semantic segmentation, and also is a smaller and more memory, time efficient model. In this paper, we focus on accelerating the SegNet-based semantic segmentation by using a model post-pruning strategy. Despite the fact that several methods have been proposed for accelerating deep models including pruning and compressing the weights of each layer, these methods may cause a certain loss of segmentation accuracy by irregular pruning. To address this issue, we propose a post-pruning strategy for deep model compression, which is commonly used to essentially deal with the over-fitting problem of decision tree. Different from some existing methods that employ the irregular pruning strategy, the proposed method can significantly improve the generalization ability of the compressed model. Inspired by the post-pruning method originally used in decision tree, our method minimizes a channel pruning loss function. The compressed model is then retrained to further improve its performance of semantic segmentation. Experimental results on two segmentation datasets show that our method obtain competitive results compared with other existing methods in terms of reducing the computational burden and improving the generalization ability of the SegNet model.

Keywords
Deep learning, Convolutional neural networks, Semantic segmentation, Model pruning

Cite this paper
Wei Liu, Accelerating SegNet-Based Semantic Segmentation Using a Model Post-Pruning Strategy , SCIREA Journal of Information Science and Systems Science. Volume 5, Issue 6, December 2021 | PP. 136-152. 10.54647/isss12188

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