Uncertainty Comparison of Visual Sensing in Adverse Weather Conditions

被引：5

作者：

Lo, Shi-Wei ^{[1
]}

Wu, Jyh-Horng ^{[1
]}

Chen, Lun-Chi ^{[1
]}

Tseng, Chien-Hao ^{[1
]}

Lin, Fang-Pang ^{[1
]}

Hsu, Ching-Han ^{[2
]}

机构：

[1] Natl Ctr High Performance Comp, 7,R&D 6th Rd,Hsinchu Sci Pk, Hsinchu 30076, Taiwan

[2] Natl Tsing Hua Univ, Dept Biomed Engn & Environm Sci, 101,Sect 2,Kuang Fu Rd, Hsinchu 30013, Taiwan

来源：

SENSORS | 2016年 / 16卷 / 07期

关键词：

vision application; outdoor imaging; visual sensing; flood detection; IMAGE; RAIN; REMOVAL; VISION; IMPLEMENTATION; LANDSLIDES; SYSTEM;

D O I：

10.3390/s16071125

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

This paper focuses on flood-region detection using monitoring images. However, adverse weather affects the outcome of image segmentation methods. In this paper, we present an experimental comparison of an outdoor visual sensing system using region-growing methods with two different growing rules-namely, GrowCut and RegGro. For each growing rule, several tests on adverse weather and lens-stained scenes were performed, taking into account and analyzing different weather conditions with the outdoor visual sensing system. The influence of several weather conditions was analyzed, highlighting their effect on the outdoor visual sensing system with different growing rules. Furthermore, experimental errors and uncertainties obtained with the growing rules were compared. The segmentation accuracy of flood regions yielded by the GrowCut, RegGro, and hybrid methods was 75%, 85%, and 87.7%, respectively.

引用

页数：18

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