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Abstract
In this paper, a method for detecting real-time images that include counterlight produced by the sun, is proposed. It involves applying a multistep analysis of the size, location, and distribution of bright areas in the image. In general, images containing counterlight have a symmetrically high brightness value at a specific location spread over an extremely large region. In addition, the distribution and change in brightness in that specific region have a symmetrically large difference compared with other regions. Through a multistep analysis of these symmetrical features, it is determined whether counterlight is included in the image. The proposed method presents a processing time of approximately 0.7 s and a detection accuracy of 88%, suggesting that the approach can be applied to a safe driving support system for autonomous vehicles.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
By recognizing in advance whether a counterlight is included in the image acquired from the vehicle black-box, malfunctions owing to image recognition errors can be prevented in advance in the application of vision-based autonomous driving vehicles, and it will be possible to apply the method as a safe driving support system
Road driving situation recognition is possible by symmetrically collecting and analyzing visual information using a CCD sensor mounted on a vehicle
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The object detection or recognition rate can be improved by conducting an image improvement on every frame of the outdoor road driving image; real-time processing becomes symmetrically difficult when applying an image improvement on every frame. To detect whether an image contains a counterlight caused by the sun, the proposed method applies a multistep analysis, such as determining the brightness value of the input image, the position of the counterlight region, and the distribution, size, and edge information. The proposed method for detecting whether the image includes a counterlight caused by sunlight uses information regarding the location of the high brightness region, the intensity histogram distribution, and the number of edge pixels. Some concluding remarks are presented in the Section 5
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