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Abstract
Image Shadow Detection and Removal in Autonomous Vehicle Based on Support Vector Machine
Highlights
Sensing systems play an important role in autonomous driving
To improve the accuracy of image recognition of driverless vehicles, we use an appropriate algorithm based on a camera to segment shadow and non-shadow effectively, and we remove the shadow and reproduce the image to improve the accuracy of image recognition of a driverless vehicle
Shadow is an important factor in the image recognition of autonomous vehicles
Summary
Sensing systems play an important role in autonomous driving. Most sensing systems are camera-based. In the research of shadow detection, by considering the reflection light of an image, Dong et al realized the accurate detection of image shadow in a new means of human–computer interaction, but this method needs manual operation and it is difficult to achieve automation.[1] Shen et al divided an image into two parts, an incident component and reflection component, by derivative classification, but this method needs a large amount of data.[2] Salvador et al calculated the average brightness based on the color constancy theory to detect the shadow area.[3] Levine and Bhattacharyya used a support vector machine (SVM) to detect shadow areas according to the brightness difference.[4] Zhou and Liu obtained the seed region for region merging using a visual significance detection algorithm, and used the region-merging algorithm to realize region merging and shadow detection. The shadow detection effect is better in a simple environment, but the experimental results of this algorithm are poor in a complex environment.[5] Sigut et al used a watershed algorithm to select seed points in LAB color space to segment an image.[6]. By transforming an RGB image into LAB color space and gray space, the shadow region is detected comprehensively by using the changes in image information in the two spaces to improve the accuracy of shadow detection, so as to achieve the effect of optimized shadow elimination
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