Sharpening and Detection of Road Studs for Intelligent Vehicles at Nighttime

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.