road lane detection.pptx

AI Based Vision Camera

1. Galgotias University Prepared by: Gautam Kumar :- 21SCSE2030077 Mrinal Dev :- 21SCSE2030018 Abhishek Kumar :- 21SCSE2030127 Road Lane Lines Detection guided by: Dr. Kavita Saini

2. ABSTRACT Introduction Advantage CANNY EDGE DETECTOR process HOUGH TRANSFORM YOLO ALGORITHM CONCLUSION CURRENT & FUTURE DEVELOPMENTS INDEX

3. Driver safety and accident reduction depend on modern cars' driver support systems. Lanes are hard to see. It estimates road-vehicle distance. This study introduces an on-board camera windscreen-viewing vision system. A automobile camera captures the front view and many methods identify lanes. The Hough transform finds lane boundaries from two fitted plots. The suggested lane detection method works on straight and curving highways, painted and unpainted, in various weather situations. The proposed method does not require lane width, crossing duration, or lane offset. Camera calibration is unnecessary. The system was tested under diverse lighting, shadow, and road situations without speed limits. The device accurately detects road lanes. ABSTRACT

4. An autonomous car is a vehicle capable of sensing its environment and operating without human involvement. A human passenger is not required to take control of the vehicle at any time, nor is a human passenger required to be present in the vehicle at all. An autonomous car can go anywhere a traditional car goes and do everything that an experienced human driver does. The basic requirement for self driving cars is to detect the lanes and keep the cars in between the lanes. INTRODUCTION

5. Advantage There are many advantages of self-Driving cars. Our roads will be safer We will be more productive We will move more efficiently

6. CANNY EDGE DETECTOR The Canny edge detection algorithm is mainly implementing the following 5 steps in order of execution 1) Applying a Gaussian filter for noise removal and image smoothening. 2) Computing the intensity gradients for all the pixels in the image. 3) Applying a process called “non-maximum suppression” to avoid unauthentic response to edge detection. 4) Applying a double-threshold categorization to evaluate edges, and determine the potential ones. 5) Evaluating edges by categorization: completing the detection of edges by removing all the other edges that are in the low category or are weak but not associated (close to or connected) to edges in the high category.

7. PROCESS

8. HOUGH TRANSFORM Hough transform can be used to detect straight lines, circles, ellipse, and other arbitrary shapes in images. It finds the location of lines in an image as given by equations The original image. Récent Patents on Computer Science

9. YOLO ALGORITHM YOLO algorithm works using the following three techniques: Residual blocks Bounding box regression Intersection Over Union (IOU) IMAGE YOLO ALGORITHM YOLO is an algorithm that uses neural networks to provide real-time object detection. This algorithm is popular because of its speed and accuracy. It has been used in various applications to detect traffic signals, people, parking meters, and animals. STEPS

10. CONCLUSION hit te Lorem ipsum dolor sit amet. Et quis nulla vel facere aliquam aut animi culpa quo quia dolorem. This study introduces " LaneRTD ," a fast and accurate lane-line recognition and tracking method. LaneRTD uses well-known methods like Canny edge detection and Hough transform. The pipeline also detects and draws lane lines to produce the final result. The proposed method requires only raw RGB images from a single CCD camera situated behind the vehicle's windscreen. Many stationary photos and real-time movies are used to test the LaneRTD . Except for one situation with complex shadow patterns, the validation results are accurate and robust. The LaneRTD's low overhead and high throughput (execution time) made it ideal for real-time lane detection. Thus, the proposed method is suitable for Advanced Driving Assistance Systems (ADAS) or self-driving cars.

11. Slide title 7 CURRENT & FUTURE DEVELOPMENTS A comprehensive discussion and analysis regarding the usefulness and the shortcomings of the proposed technique as well as suggestions for improvements and future work are presented.

12. Thank you