Deep Learning Using Computer Vision in Self-driving Cars for Traffic Sign Detection
Keywords:
Artificial intelligence, Autonomous driving, Image processing, Computer vision technologyAbstract
Two key aspects of this paper are lane detection and vehicle and obstacle detection using cameras. In lane detection, research has focused on several different methods and techniques. Aziz et al. (2017) proposed a lane detection model based on colour region, line selection, edge selection, and Hough transformation. The method uses computer vision and sensor fusion, combined with path planning technology, to help autonomous vehicles stay in a specific lane or switch to another lane on the road. First, the method captures road images and then performs a colour selection, mask, and edge detection process to identify and track lanes accurately. However, this algorithm, based on changes in image brightness, can perform poorly at night when the light is dim because the image contrast is low, leading to difficulties in lane detection. In summary, the application of these research results in autonomous driving technology provides a variety of innovative methods and technologies for lane detection and vehicle and obstacle detection, which helps to improve the safety and performance of autonomous driving systems.
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