Abstract
Intelligent vehicles need to survive against various environments on roads such as sunlit, unclear, showery, shadowy, and inside tunnel conditions. This research designs a robust approach for detecting road studs at nighttime, which is the combination of various statistical methods. This detection approach is a unique approach developed for the detection of road lanes instead of road-painted lanes. Therefore, we detect road studs (cat eyes) instead of the painted lanes on roads at nighttime as the road studs have higher intensities at nighttime. First, we utilized Butterworth low-pass filter in order to sharpen the images. Second, we converted the image to grayscale and extracted the corresponding region of interest (ROI) from it. Then, the Canny edge detection algorithm was applied to create boundary lines in images. Finally, the Hough transform was applied to detect the desired lanes in the images, which are the road studs, and hence we successfully detected the road studs in images. We have used our own dataset for the stud’s detection, which considered most of the limitations of the previous datasets. Also, the dataset was collected in naturalistic environments at nighttime. The experimental result presents that the designed approach is accurate and robust for road stud detection against nighttime.
Highlights
Vehicles have a vital role in technology development
We utilized Butterworth low-pass filter in order to sharpen the image. en, we converted the image to grayscale and extracted the corresponding region of interest (ROI) from it. en, the Canny edge detection algorithm is applied to create boundary lines in the image. en, the Hough transform is applied in order to detect the desired lanes in the image, which are the road studs, and we successfully detected the road studs in the image. e proposed work has been implemented in Python using the OpenCV library. e results signify that road studs have a higher intensity than road-painted lanes at nighttime and can help to recognize the studs
We utilized the Butterworth low-pass filter in order to sharpen the images. en, the conventional Canny edge detector and Hough transform (HT) were employed in order to detect the road studs at nighttime in an image. is research designs a robust approach for the detection of road studs at nighttime, which is the combination of various statistical methods
Summary
One of the most advanced technologies is autonomous vehicles that are self-driving vehicles on roads. E autonomous vehicles track and detect roadpainted lanes, and with the help of those painted lanes (yellow and white), the vehicles keep driving on the road. E vehicles have embedded devices such as cameras, GPS, light detection and ranging (LIDAR), and sensors; through these devices, the autonomous vehicles track and detect road edges and reach the destination. Still, it is a solicitous problem because of different road environments such as sunny, shadowy, nighttime, and inside tunnel conditions. Road lane detection might suffer from various problems such as the nighttime scene, shadows, and lighting fluctuation [3]
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