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Adaptive Exposure Control for Line-Structured Light Sensors Based on Global Grayscale Statistics.
Yuehua Li1, Qingfeng Zhao1, Po Hu2
1School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Sensors (Basel, Switzerland)
|February 26, 2025
Summary
This study introduces an adaptive exposure method for line-structured light sensors, improving 3D measurement quality. The new approach enhances measurement accuracy and point density for diverse object surfaces.
Area of Science:
- Optics and Photonics
- Computer Vision
- Metrology
Background:
- Line-structured light sensors are vital for 3D measurements.
- Consistent stripe image quality is essential for accurate data acquisition.
- Variations in object properties (shape, material, color) challenge measurement consistency.
Purpose of the Study:
- To develop an adaptive exposure method for line-structured light sensors.
- To enhance measurement effectiveness across diverse object characteristics.
- To improve the quality and accuracy of 3D measurements.
Main Methods:
- Global grayscale statistical analysis of stripe images.
- Calculation of a quality evaluation parameter based on the logarithm sum of grayscale statistics.
- Development of an adaptive exposure control system linked to the quality parameter.
- Analysis of control system parameter influence on measurement outcomes.
Main Results:
- The proposed quality evaluation value shows a near-linear correlation with camera exposure time.
- The adaptive exposure method successfully adjusts camera exposure for varying surface properties.
- Significant improvements in both the number of effective measurement points and overall accuracy were observed.
Conclusions:
- The adaptive exposure method effectively optimizes stripe image acquisition for line-structured light sensors.
- This technique enhances 3D measurement performance for objects with diverse optical properties.
- The findings contribute to more robust and accurate 3D scanning applications.
