A Cityscape Image Detail Extraction Enhancement Method for Lightweight Semantic Segmentation

Xinhe Yu, Huarong Xu, Lifen Weng
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引用次数: 1

Abstract

Lightweight semantic segmentation is widely used in automotive driving. But the existing methods lack the ability to extract the detailed features of urban street scenes, and the semantic segmentation network structure lacks the logical relationship of interdependence. In order to improve semantic segmentation performance in automotive driving, this paper is based on BisenetV2 to propose: (1) The re-parametrization strategy to improve the ability to extract details features. (2) The SENet channel attention mechanism is adopted to explicitly establish the interdependence between feature channels. (3) Using the larger kernel in the deep layer of the network structure increases the accuracy of semantic segmentation and hardly affects the calculated amount. We tested the Cityscapes test dataset to achieve 72.23% mIoU at 2048×1024 resolution with the speed of 39.55 FPS on one NVIDIA RTX A5000 card without pre-training and accelerated implementations like TensorRT, which is 1.8% more accurate than the latest methods while almost as fast.
基于轻量级语义分割的城市景观图像细节提取增强方法
轻量语义切分在汽车驾驶中有着广泛的应用。但现有方法缺乏对城市街景细节特征的提取能力,语义分割网络结构缺乏相互依存的逻辑关系。为了提高汽车驾驶中的语义分割性能,本文基于BisenetV2提出:(1)重新参数化策略,提高细节特征提取能力。(2)采用SENet通道注意机制,明确建立特征通道之间的相互依赖关系。(3)在网络结构的深层使用更大的核,提高了语义分割的准确性,几乎不影响计算量。我们测试了cityscape测试数据集,在2048×1024分辨率下以39.55 FPS的速度在一块NVIDIA RTX A5000卡上实现了72.23%的mIoU,而无需预训练和像TensorRT这样的加速实现,其准确率比最新方法高1.8%,同时速度几乎一样快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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