LANE-LINE DETECTION SYSTEM IN PYTHON USING OPEN CV

ABSTRACT

Being able to detect lane lines could be a crucial task for any self-driving autonomous vehicle. In this project, to identify lane lines on the road OpenCV is used. OpenCV method uses the input images to find any lane lines command among and also for rendering out an illustration of the lane. The OpenCV tools like colour selection, the region of interest selection, grey scaling, Gaussian smoothing, Canny Edge Detection, and Hough Transform line detection are being employed. A colour detection algorithm identifies pixels in a picture that matches a given colour or colour range. Region of interest selection allows you to select a rectangle in an image, crop the rectangular region and finally display the cropped image. Grey scaling is the method of changing an image from different colour spaces e.g. RGB, CMYK, HSV, etc. to shades of grey. In gaussian Blur operation, the image is convolved with a mathematician filter rather than the box filter. The Gaussian filter could be a low-pass filter that removes the high-frequency elements. Canny Edge Detection is used to detect the edges in a picture. It accepts a grayscale image as input and it uses a multi-stage algorithm. The Hough Transform line is a method that is used in image processing to detect any shape if that shape can be represented in mathematical form. The goal is to piece along a pipeline to detect the line segments within the image, then average/extrapolate them and draw them onto the image for the show.

Chapter 1: Introduction

With the rapid development of society, automobiles have become one of the transportation tools for people to travel. In the narrow road, there are more and more vehicles of all kinds [1] . As more and more vehicles are driving on the road, the number of victims of car accidents is increasing every year [2] . How to drive safely under the condition of numerous vehicles and narrow roads has become the focus of attention. Advanced driver assistance systems which include lane departure warning (LDW) [3] , Lane Keeping Assist, and Adaptive Cruise Control (ACC) [4] can help people analyse the current driving environment and provide appropriate feedback for safe driving or alert the driver in dangerous circumstances. This kind of auxiliary driving system is expected to become more and more perfect [5] . However, the bottleneck of the development of this system is that the road traffic environment is difficult to predict [6] . After investigation, in the complex traffic environment where vehicles are numerous and speed is too fast, the probability of accidents is much greater than usual. In such a complex traffic situation, road colour extraction and texture detection as well as road boundary and lane marking are the main perceptual clues of human driving

Lane detection is a hot topic in the field of machine learning and computer vision and has been applied in intelligent vehicle systems [8] . The lane detection system comes from lane markers in a complex environment and is used to estimate the vehicle’s position and trajectory relative to the lane reliably [9] . At the same time, lane detection plays an important role in the lane departure warning system. The lane detection task is mainly divided into two steps: edge detection and line detection.

Line detection is as important as edge detection in lane detection. With regard to line detection, we usually have two methods which include feather-based method and model-based methods. Hence, in this study, we propose a method for  lane-line detection system in python using Opencv.

1.2 Statement of Problem

This thesis will investigate different techniques for road and lane detection and how they can be implemented. Our project will be an element of a more extensive project, whose purpose is to create a prototype of an autonomous vehicle. In order to achieve this we need to provide the vehicle with an awareness of the surroundings. To create an artificial computer vision, one tries to imitate functions that the human vision provides. By analyzing digital frequencies in detail from images, the computer can create a visual understanding and act accordingly.

1.3 Purpose of the Study

The purpose of this project is to investigate different lane keeping models and implement the most suitable with the input of a RGB camera. The lane keeping algorithm should be able to find curved lanes, be able to communicate with other program’s as well as give an estimated direction angle to make the car position itself between two lines. The goal is to create a narrow AI that’s capable of recreate the function of an human’s vision. The obtained stream of frames from the camera is analyzed and processed, with the intention of extracting certain information of the surroundings. The information should be used as an input to the lane detection algorithm, to create an estimated direction angle.

1.4 Aims and Objective

The general objective of this study is to develop a lane-line detection system in python using Opencv. The specific objectives include;

1. Investigate a suitable algorithm for lane keeping based on our initial goals.
2. Determine which camera that is suitable for capturing data while moving.
3. Determine how should the main neural network and the lane detection algorithm, written in different languages efficiently communicate in real time.

1.5 Delimitation of the Study

The main goal of the project is to investigate existing solutions regarding lane detection and lane keeping and implement an own solution that should be able to detect road lines on slightly curved roads or straight roads, and be able to calculate an estimated direction angle. The lane keeping algorithm will only manage straight and curved lines with good conditions such as marked lanes, beneficial light and limited noise.

LANE-LINE DETECTION SYSTEM IN PYTHON USING OPEN CV

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