Zhang, Guangdong, Wang, Guangwei, Chen, Jinhua, Jiang, Wei, Hao, Xinyu and Deng, Tong ORCID: https://orcid.org/0000-0003-4117-4317 (2025) An automatic control system based on machine vision and deep learning for car windscreen clean. Scientific Reports, 15 (4857). ISSN 2045-2322 (Online) (doi:10.1038/s41598-025-88688-9)

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Abstract

Raindrops on the windscreen significantly impact a driver’s visibility during driving, affecting safe driving. Maintaining a clear windscreen is crucial for drivers to mitigate accident risks in rainy conditions. A real-time rain detection system and an innovative wiper control method are introduced based on machine vision and deep learning. An all-weather raindrop detection model is constructed using a convolutional neural network (CNN) architecture, utilising an improved YOLOv8 model. The all-weather model achieved a precision rate of 0.89, a recall rate of 0.83, and a detection speed of 63 fps, meeting the system’s real-time requirements. The raindrop area ratio is computed through target detection, which facilitates the assessment of rainfall begins and ends, as well as intensity variations. When the raindrop area ratio exceeds the wiper activation threshold, the wiper starts, and when the area ratio approaches zero, the wiper stops. The wiper control method can automatically adjust the detection frequency and the wiper operating speed according to changes in rainfall intensity. The wiper activation threshold can be adjusted to make the wiper operation more in line with the driver’s habits.

Item Type:	Article
Uncontrolled Keywords:	raindrop detection, deep learning, CNN, driving safety, wiper automatic control system
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General) T Technology > TS Manufactures
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG) Faculty of Engineering & Science > Wolfson Centre for Bulk Solids Handling Technology
Last Modified:	17 Jul 2025 11:10
URI:	https://gala.gre.ac.uk/id/eprint/50820

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