Abstract
In recent years, with improvement of photoelectric conversion efficiency and accuracy, photoelectric sensor was arranged to simulate binocular stereo vision for 3D measurement, and it has become an important distance measurement method. In this paper, an improved sum of squared difference (SSD) algorithm which can use binocular cameras to measure distance of vehicle ahead was proposed. Firstly, consistency matching calibration was performed when images were acquired. Then, Gaussian blur was used to smooth the image, and grayscale transformation was performed. Next, the Sobel operator was used to detect the edge of images. Finally, the improved SSD was used for stereo matching and disparity calculation, and the distance value could be obtained corresponding to each point. Experimental results showed that the improved SSD algorithm had an accuracy rate of 95.06% when stereo matching and disparity calculation were performed. This algorithm fully meets the requirements of distance measurement.
Highlights
A photoelectric sensor is a semiconductor device which can convert light signals into electrical signals based on photoelectric effect
This algorithm required additional sensors to assist in attitude detection, which could increase the complexity of the system
A method based on the parallel binocular vision system and similarity judgment function was established by using cluster analysis method, and the distance was calculated by combining features of gradient histogram and cascade classifier [12]
Summary
A photoelectric sensor is a semiconductor device which can convert light signals into electrical signals based on photoelectric effect. The algorithm mainly includes five algorithms: gray-scale transformation, Gaussian Blur transformation, edge detection by Sobel operator, model of a binocular stereo camera, improved Sum of Squared Difference. It can accurately perform stereo matching and distance measurement. Improved SSD was used to perform stereo matching and disparity calculation on a target image, and the distance value could be obtained corresponding to each point, which was combined into a distance value matrix. In order to verify the accuracy and performance of our algorithm, an experimental platform was used to control the binocular camera at a specific distance from the target, and image acquisition was performed. It had a higher accuracy and smaller accuracy error fluctuations, which meets actual measurement requirements
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