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A Comparative Study on the Machine Vision Realism of Rainfall Simulation Methods
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Junyi Chen1, Tian Xia1, Zhenyuan Liu1, Tong Jia1, Xiaoyi Wang2, Xuehan Ma2, Xingyu Xing1, Jianfeng Wu1
Automotive Engineering | 2025, 47(3) : 449 - 459
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Automotive Engineering | 2025, 47(3): 449-459
Feature Topic:Key Technologies on Intelligent and Connected Vehicles
A Comparative Study on the Machine Vision Realism of Rainfall Simulation Methods
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Junyi Chen1, Tian Xia1, Zhenyuan Liu1, Tong Jia1, Xiaoyi Wang2, Xuehan Ma2, Xingyu Xing1, Jianfeng Wu1
Affiliations
  • 1 School of Automotive Studies,Tongji University,Shanghai 201804
  • 2 Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co.,Ltd.,Shanghai 201805
Published: 2025-03-25 doi: 10.19562/j.chinasae.qcgc.2025.03.007
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Given the high exposure and risk of rainfall as a trigger condition for visual perception systems, various rainfall simulation tests are the main research methods. However, the realism of rain simulation of different testing methods impacts the confidence in test conclusions. In this study indicators are selected to quantify the impact of rainfall on machine vision from the aspects of image quality and object detection. Using the numerical range and trend of index changes under real rainfall as a benchmark, the comparative study of the realism of different rainfall simulation methods in the dimension of machine vision is carried out. Additionally, in this study 1 950 images of no rain and various levels of real rainfall are collected to construct a dataset, so as to obtain statistical patterns of the impact of real rainfall on machine vision. Two simulated rainfall test sites, three simulation software, and one generative model are selected for rainfall simulation tests to compare and analyze the realism of different types of rainfall simulation methods horizontally. The results show that, in terms of image quality, simulation software and rainfall simulation equipment can better simulate the real rain in terms of DR value range and trend. Regarding target detection, simulation software and generative model are closer to real rainfall in terms of CC change values. Overall, in terms of realism, digital simulation of rainfall performs best, followed by physical rainfall simulation on site and generative model, providing a reference for testing the SOTIF of the visual perception system of intelligent and connected vehicles.

rainfall simulation  /  realism assessment  /  machine vision  /  visual perception system
Junyi Chen, Tian Xia, Zhenyuan Liu, Tong Jia, Xiaoyi Wang, Xuehan Ma, Xingyu Xing, Jianfeng Wu. A Comparative Study on the Machine Vision Realism of Rainfall Simulation Methods[J]. Automotive Engineering, 2025 , 47 (3) : 449 -459 . DOI: 10.19562/j.chinasae.qcgc.2025.03.007
Year 2025 volume 47 Issue 3
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Article Info
doi: 10.19562/j.chinasae.qcgc.2025.03.007
  • Receive Date:2024-08-07
  • Online Date:2025-07-09
  • Published:2025-03-25
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  • Received:2024-08-07
  • Revised:2024-09-18
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    1 School of Automotive Studies,Tongji University,Shanghai 201804
    2 Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co.,Ltd.,Shanghai 201805
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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