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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
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Sensors and Materials, Volume 32, Number 6(1) (2020)
Copyright(C) MYU K.K.
pp. 1969-1979
S&M2233 Research Paper of Special Issue
Published: June 10, 2020

Image Shadow Detection and Removal in Autonomous Vehicle Based on Support Vector Machine [PDF]

Tianjun Zhu and Xiaoxuan Yin

(Received December 26, 2019; Accepted April 28, 2020)

Keywords: support vector machine, shadow detection, autonomous vehicle

An image shadow in an autonomous vehicle often causes failures in image segmentation and object tracking and in recognition algorithms. In this paper, a shadow detection method based on a support vector machine (SVM) is proposed. Firstly, an RGB image was converted to LAB color space, and a shadow detection model based on an SVM was obtained by training the image with a shadow. Then, the image was divided into a shadow region, a shadow boundary, and a light region. Moreover, the light intensity in the shadow region was adjusted by eliminating the pixel difference between the shadow region and the light region. Meanwhile, the image gradient was established within the shadow boundary, and the boundary shadow was replaced by smooth interpolation to achieve a smooth transition from the light region to the shadow region. Finally, a clear image without a shadow was recovered using wavelet gradient data. Experimental results show that this method can detect the shadow region in an image and reproduce the image without the shadow effectively.

Corresponding author: Tianjun Zhu

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Tianjun Zhu and Xiaoxuan Yin, Image Shadow Detection and Removal in Autonomous Vehicle Based on Support Vector Machine, Sens. Mater., Vol. 32, No. 6, 2020, p. 1969-1979.

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