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A Live Lane-Line Detection Systems built-in Python language is another Data Science project idea for beginners. A human driver receives lane detecting instruction from lines placed on the road in this project. The lines placed on the roads indicate where the lanes are located for human driving. It also refers to the vehicle’s steering direction. This application is crucial for the development of self-driving cars. This application for the Data Science Project is critical for the development of self-driving cars.
Lane Line detection is a critical component for self driving cars and also for computer vision in general. This concept is used to describe the path for self-driving cars and to avoid the risk of getting in another lane.
In this article, we will build a machine learning project to detect lane lines in real-time. We will do this using the concepts of computer vision using OpenCV library. To detect the lane we have to detect the white markings on both sides on the lane.
Using computer vision techniques in Python, we will identify road lane lines in which autonomous cars must run. This will be a critical part of autonomous cars, as the self-driving cars should not cross it’s lane and should not go in opposite lane to avoid accidents.
To detect white markings in the lane, first, we need to mask the rest part of the frame. We do this using frame masking. The frame is nothing but a NumPy array of image pixel values. To mask the unnecessary pixel of the frame, we simply update those pixel values to 0 in the NumPy array.
After making we need to detect lane lines. The technique used to detect mathematical shapes like this is called Hough Transform. Hough transformation can detect shapes like rectangles, circles, triangles, and lines.
When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes are act as our constant reference for where to steer the vehicle. Naturally, one of the first things we would like to do in developing a self-driving car is to automatically detect lane lines using an algorithm.
In this project you will detect lane lines in images using Python and OpenCV. OpenCV means "Open-Source Computer Vision", which is a package that has many useful tools for analyzing images.
The tools we have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection. Our goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display. Once we have a working pipeline, we will try it out on the video stream.
The traffic safety becomes more and more convincing with the increasing urban traffic. Exiting the lane without following proper rules is the root cause of most of the accidents on the avenues. Most of these are result of the interrupted and lethargic attitude of the driver. Lane discipline is crucial to road safety for drivers and pedestrians alike. The system has an objective to identify the lane marks. It’s intent is to obtain a secure environment and improved traffic surroundings. The functions of the proposed system can range from displaying road line positions to the driving person on any exterior display, to more convoluted applications like detecting switching of the lanes in the near future so that one can prevent concussions caused on the highways
Computational Results of Deep Learning Approach Pixel wise semantic segmentation is used for image segmentation. Dataset used for the training is taken from Internet sources. 30 Classes have been used for segmentation. Simple convolution diluted with activation function- Prelu, is used. Finding the kernel is the main approach. Various classes can be used for the purpose of segmentation like: Road People Vehicle Terrain In this project we need the road class for segmentation. Below Table 3.1 depicts the model of Enet pipeline schema.
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