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Article type: Research Article
Authors: Zhao, Xianhaoa | Wang, Mingyanga; * | Xin, Chaoquna | Wang, Xianjieb
Affiliations: [a] School of Computer and Control Engineering, Northeast Forestry University, Harbin, China | [b] Harbin Institute of Technology, Harbin, China
Correspondence: [*] Corresponding author. Mingyang Wang, School of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China. E-mail: [email protected].
Abstract: In the field of autonomous driving, driving systems need to understand and quickly respond to changes in road scenes, which makes it equally important to enhance the accuracy and real-time performance of semantic segmentation tasks in road scenes. This article proposes a lightweight road scene semantic segmentation model LR3S that integrates global contextual information based on the DeepLabV3+ framework. LR3S utilizes a lightweight GhostNetV2 network as the backbone to capture rich semantic information in images, and uses ASPP_eSE module to enhance the capture of multi-scale and detail level semantic information. In addition, a lightweight CARAFE upsampling operator is utilized to upsample feature maps, taking advantage of CARAFE’s large receptive field and low computational cost to prevent the loss of fine-grained features and ensure the integrity of semantic information. Experimental results demonstrate that LR3S achieves an MIoU of 74.47% on the Cityscapes dataset and obtains an MIoU of 76.01% on the PASCAL VOC 2012 dataset. Compared to baseline semantic segmentation models, LR3S significantly reduces the parameter amount while maintaining segmentation accuracy, achieving a good balance between model accuracy and real-time performance.
Keywords: Semantic segmentation, road scenes, attention mechanism, GhostNetV2, CARAFE
DOI: 10.3233/JIFS-239692
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
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