Lane Detection on Roads using Computer Vision
Abhishek Goyal1, Mridula Singh2, Anand Srivastava3

1Dr. Abhishek Goyal*, Uttarakhand Technical University, Sudhowala (Uttarakhand), India.
2Dr. Mridula Singh, Uttarakhand Technical University, Sudhowala (Uttarakhand), India.
3Anand Srivatava , Uttarakhand Technical University, Sudhowala (Uttarakhand), India.
Manuscript received on September 21, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 1200-1205 | Volume-9 Issue-1, October 2019 | Retrieval Number: A94256109119/2019©BEIESP | DOI: 10.35940/ijeat.A94256.109119
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Abstract: In recent times many technological advancements are coming in the domain of road safety as accidents has been increasing at an alarming rate and one of the crucial reason for such accidents is lack of driver’s attention. Technical advancements should be there to reduce the frequency of the accidents and stay safe. One of the way to achieve the same is through Lane Detection Systems which work with the intention to recognize the lane borders on road and further prompts the driver if he switches and moves to erroneous lane markings. Lane detecting system is an essential component of many technologically intelligent transport system. Although it’s a complex goal to achieve because of vacillating road conditions that a person encounters specially while driving at night or even in daylight. Lane boundaries is detected using a camera that captures the view of the road, mounted on the front of the vehicle. The approach used in this paper changes the image taken from the video into a set of sub-images and generates image-features for each of them which are further used to detect the lanes present on the roads. There are proposed numerous ways to detect the lane markings on the road. Feature-based or model-based are the two categories of the lane detection techniques. Down-level characteristics for example lane-mark edges are used by the feature-based functions.
Keywords: Traffic Safety, Lane Detection, Deep Learning, Computer Vision.