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Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Updated: May 2, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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An automatic control system based on machine vision and deep learning for car windscreen clean.

Guangdong Zhang¹, Guangwei Wang², Jinhua Chen²

¹School of Mechanical Engineering, Yancheng Institute of Technology, Yancheng, 224051, China. gdzhang@ycit.edu.cn.

Scientific Reports

|February 9, 2025

Summary

This study introduces an AI-powered system for real-time rain detection and automatic wiper control, enhancing driving safety in adverse weather conditions. The innovative approach ensures a clear windscreen by intelligently managing wiper activation and speed based on rainfall intensity.

Keywords:

CNN Deep learning Driving safety Raindrop detection Wiper automatic control system

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Area of Science:

Computer Vision
Machine Learning
Automotive Safety

Background:

Driver visibility is significantly impaired by raindrops on windscreens, increasing accident risks.
Maintaining a clear windscreen is essential for safe driving during rainfall.
Existing wiper systems may lack real-time adaptability to varying rain intensities.

Purpose of the Study:

To develop a real-time rain detection system for improved driver visibility.
To create an innovative, intelligent wiper control method based on machine vision and deep learning.
To enhance automotive safety by mitigating risks associated with reduced visibility in rainy conditions.

Main Methods:

An all-weather raindrop detection model was built using a Convolutional Neural Network (CNN) with an improved YOLOv8 architecture.
The system computes the raindrop area ratio via target detection to assess rainfall onset, cessation, and intensity.
An adaptive wiper control method was implemented, adjusting wiper speed and activation based on the detected raindrop area ratio.

Main Results:

The all-weather raindrop detection model achieved a precision of 0.89, a recall of 0.83, and a detection speed of 63 frames per second (fps).
The system accurately detects rainfall start/end and intensity variations using the raindrop area ratio.
The wiper control system demonstrated real-time responsiveness, activating/deactivating and adjusting speed based on rainfall intensity.

Conclusions:

The developed machine vision and deep learning system effectively detects raindrops and controls wipers in real-time.
The system enhances driving safety by ensuring a clear windscreen during rainy conditions.
The adaptive wiper control, with adjustable thresholds, offers a personalized and efficient solution for drivers.