@article{ref1,
title="Road-scene parsing based on attentional prototype-matching",
journal="Sensors (Basel)",
year="2022",
author="Chen, Xiaoyu and Wang, Chuan and Lu, Jun and Bai, Lianfa and Han, Jing",
volume="22",
number="16",
pages="e6159-e6159",
abstract="Road-scene parsing is complex and changeable; the interferences in the background destroy the visual structure in the image data, increasing the difficulty of target detection. The key to addressing road-scene parsing is to amplify the feature differences between the targets, as well as those between the targets and the background. This paper proposes a novel scene-parsing network, Attentional Prototype-Matching Network (APMNet), to segment targets by matching candidate features with target prototypes regressed from labeled road-scene data. To obtain reliable target prototypes, we designed the Sample-Selection and the Class-Repellence Algorithm in the prototype-regression progress. Also, we built the class-to-class and target-to-background attention mechanisms to increase feature recognizability based on the target's visual characteristics and spatial-target distribution. Experiments conducted on two road-scene datasets, CamVid and Cityscapes, demonstrate that our approach effectively improves the representation of targets and achieves impressive results compared with other approaches.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22166159",
url="http://dx.doi.org/10.3390/s22166159"
}