Real-time Robust Lane Detection and Warning System using Hough Transform Method

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Real-time Robust Lane Detection and Warning System using Hough Transform Method

Prajakta R. Yelwande1 1MIT World Peace University, Pune, India

Prof. Aditi Jahagirdar2 2MIT College of Engineering, Pune, India

Abstract- Many people die each year in roadway departure crashes caused by driver inattention. Lane detection systems are useful in avoiding these accidents as safety is the main purpose of these systems. Such systems have the target to detect the lanes and to warn the driver in case the vehicle has a tendency to depart from the lane. A lane detection system is an important aspect of many intelligent transport systems. Lane detection is a demanding task because of the varying road conditions that one can come across while driving. In the past few years, plentiful approaches for lane detection were proposed and successfully demonstrated. In this paper, after a brief overview of existing methods, we present a robust lane detection based on Canny edge detection and Hough

transform method.

Keywords Canny Algorithm, Edge Detection, Feature Extraction, Hough Transform, Lane Detection, Region of Interest(ROI).

  1. INTRODUCTION

    Now a day the road accidents have increased to a great extent. Most of the accidents occur due to drivers negligence and carelessness while driving. Advance driver assistance system (ADAS) plays an important role in providing safety to drivers. It helps to automate the car system and increases the driving experiences. The Advance driver assistance system (ADAS) provides a safe system to reduce the road accidents. The system takes an vigorous step like warning the driver or takes a corrective action to avoid an accident during the risky situation.

    The Lane Departure Warning (LDW) is an important unit in Advance driver assistance system. In vision based lane departure system, a camera is placed behind the wind shield of the vehicles and images of the road is captured. The white stripes on the road are interpreted and lanes are identified. Whenever the vehicle goes out of the lane then the warning is given to

    the driver. In lane departure warning system, the lane detection is the primary step to be taken.

    There are two types of approaches used in lane detection: the feature based approach and the model based approach. The features based approach detects the lane in the road images by detecting the low level features such as lane edges or highlighted lanes etc. This approach requires well highlighted lines or strong lane edges, otherwise it will fail. This approach may suffer from occlusion or noise. The geometric parameters such as assuming the shape of lane can be presented by straight line or curves are used by the model based approached.

  2. LITERATURE OVERVIEW

    Most of the time accidents are caused by lack of concentration and not maintaining a safe car distance to the car in front, or changing lanes without paying attention for vehicles which is next to the car. This project is about detecting the boundaries of the lane and to tell the driver if he/she is going to change the lane without signifying for his/her intention. The system should also try to measure the distance to the vehicle in front of that vehicle and signalize if the distance in not safe enough.

    Lane detection in driving scenes is an significant component for autonomous vehicles and advanced driver assistance systems. In recent years, many complicated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unacceptable performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are incessant line structures on the road.

    Vehicle safety plays an important role for safety of all road users and also useful to measure the crash avoidance or reduction of injury. The purposes of Advanced driver assistance systems are to reduce the risk and assist post impact care are also investigated for future application.

    Table 1: Literature Survey

    Sr. No

    Paper Reference No.

    Year

    Methods Used

    Advantages

    Accuracy

    1.

    [1]

    2018,

    IET Jour.

    CNN, pre-processing,

    feature detection, fitting tracking, kalman filter, particle filter.

    It gives high accuracy.

    Better results for detecting curved lanes.

    98%

    2.

    [2]

    2018, IEEE

    Feature extraction,

    model fitting, Random Sample Consensus (RANSAC) technique

    Better computation efficiency, High accuracy

    3.

    [3]

    2018, IEEE

    Principle Component Analysis Technique

    Real-time performance within a low computation hardware platform

    4.

    [4]

    2018, IEEE

    Median strip detection approach, Lane change detection approach

    Smart use of spatio-temporal information provided by the embedded sensors technology

    5.

    [5]

    2018, IJPAM

    Journal

    Review paper

    6.

    [6]

    2018, EURASIP

    Journal

    Hough transform and Kirsch operator, feature extraction

    the robustness and adaptability of the detection results are enhanced, the computational complexity of the algorithm is reduced by the matrix operation.

    7.

    [7]

    2018,

    Hindawi Journal

    Kalman filter, Hough transform, Feature extraction, colour extraction

    Better accuracy and faster processing speed

    95%

    8.

    [8]

    2017

    LDWS Algorithm, Canny's Algorithm,

    Hough Transform Technique

    High accuracy and robustness against noise and model imperfection

    9.

    [9]

    2017, IEEE

    Canny edge detection algorithm, Hough transform Method

    Faster processing speed

    10.

    [10]

    2017, IEEE

    Gabor filter,

    Hough transform method, Sobel operator, least squares algorithm

    System is real-time, efficient and enhance the adaptability for the changing environment of road scene.

    93%

    11.

    [11]

    2017, IEEE

    Spatio-Temporal incremental clustering algorithm, PCA technique

    Accurately detects straight as well as curved lanes, Algorithm does not require database for storing images

    95%

    12.

    [12]

    2017, IEEE

    FPGA system

    System is useful to monitor the vehicle to track online the vision detection lane mark and execute obstacle avoidance.

    13.

    [13]

    2017, IEEE

    Hough transform, morphological operations

    Detecting straight as well as curved roads of hilly areas using vision based techniques.

    81.67%

    14.

    [14]

    2017, IEEE

    Histograms of oriented gradients, SVM Classifier, kalman filter

    Accurately detects straight as well as curved lanes

    96.3%

    15.

    [15]

    2017, IEEE

    Mono- vision based lane detection technique, Sobel filter

    Addressed the problem of the generation of an optimal constrained lane reference to be tracked by the automated guided vehicle.

    16.

    [16]

    2017, IEEE

    Hough- transform, RANSAC Bezier splines fitting, Gaussian filter

    Able to find vehicles in front of our vehicle like cars, buses but unable to find two wheelers.

    85%

    17.

    [17]

    2017, IEEE

    Kalman filter, SVM Classifier

    High Accuracy

    98.1%

    18.

    [18]

    2017, IEEE

    Canny algorithm, Sobel operator, Hough transform

    Can detects linear lanes based on Hough transform

    19.

    [19]

    2017, IEEE

    Feature extraction

    Detects lanes in different environment conditions

    20.

    [20]

    2017, ICROS

    Kalman filter

    Accurately detects straight lanes

    21.

    [21]

    2017

    Randomized Hough Transform

    Good accuracy for straight roads

    22.

    [22]

    2017

    Sobel filter, Hough Transform for Lane Detection

    Hough transform was still able to track the loss of lane marks by assuming the lane was still there by counting the number of the lost frame. If the lost track is more than the defined number of frames, then it stopped the Tracking operation.

    23.

    [23]

    2017

    fuzzy c-means for Segmentation, modifying the Hough transform i.e. hybridization of additive Hough transform with artificial bee colony edge detection to detect curve lanes

    In this modify Hough transform i.e. additive Hough transform with artificial bee colony based edge detector is used to get better straight lane as well as curved lane road images

    24.

    [24]

    2017

    Ellipsoidal Neural Networks with Dendrite Processing (ENNDPs)

    We have shown how the proposed methodology can be successfully applied to Automatically detect lanes in urban highways.

    25.

    [25]

    2017

    Hough Transform

    This system will work in both day and night situation

    26.

    [26]

    2017

    Lane coloration Algorithm (modifying the Hough transform i.e. fuzzy logic)

    Fuzzy Logic is used to improve straight lane as well as curved lane road images

    27.

    [27]

    2016, IEEE

    Phase angle varying range (PAVR) to achieve a better position judging

    Analyzes the edge position detection method of segmental wireless power supply for electrical vehicles without position sensors

    28.

    [28]

    2016, IEEE

    CNNs, Hough transform, Canny operator

    System can achieve higher recall and accuracy in real scenes videos

    90.7%

    29.

    [29]

    2016, IEEE

    Speed-adaptive ratio based algorithm

    Can predict the speed-adaptive lateral ratio between left and right lanes

    30

    [30]

    2016, IEEE

    SVM model

    Can detect the normal and abnormal lane changes instances

    90%

    31.

    [31]

    2016

    RFID, V-I Positioning algorithm

    32.

    [32]

    2016, IJRITCC

    Journal

    Hough transform, Vanishing point based boundary detection

    Good results, both straight and slightly curved road are detected.

    33.

    [33]

    2015

    Gaussian mixture model, RANSAC method

    Detects lanes even in sunny and shadow road

    95.7%

    34.

    [34]

    2015, IRJET

    Journal

    Hough transform, Bilateral filter, Canny edge detector

    Optimal edge detection

    35.

    [35]

    2014, IJCSMC

    Journal

    Review Paper

    36.

    [36]

    2013, IEEE

    RANSAC Model, Kalman filter

    Faster processing speed, good performance of the system.

    93.2%

    37.

    [37]

    2006, IEEE

    Review Paper

    38.

    [38]

    Hough transform, Gaussian filter

    Faster processing speed (123 m/sec)

    39.

    [39]

    2018, IEEE(AJCT

    Journal)

    Review Paper

    40.

    [40]

    2019

    Hough transform, sobel operator

    Better accuracy

    96%

  3. SYSTEM ARCHITECTURE

    Fig. 2. Proposed Architecture of lane detection system

    System is based on following steps:

    1. Video: Live video is captured using camera fixed in vehicle.

    2. Frame Conversion: Frames can be obtained from a video and converted into images.

    3. Edge detection: sudden changes of discontinuities in an image are called as edges. Significant transitions in an image are called as edges. Most of the shape information of an image is enclosed in edges. So first we detect these edges in an image and by using these filters and then by enhancing those areas of image which contains edges, sharpness of the image will increase and image will become clearer.

    4. Hough Transform: The Hough Transform (HT) is a robust method for finding lines in images that was developed by Paul Hough.

    5. Lane Detection: Hough Transform is a popular technique to detect any shape, if you can represent that shape in mathematical form. It can detect the shape even if it is broken or distorted a little bit.

    6. Car Detection: Object Recognition is a computer technology that deals with image processing and computer vision, it detects and identifies objects of various types such as humans, animals, fruits & vegetables, vehicles, buildings etc..Every object in existence has its own unique characteristics which make them unique and different from other objects. RNN (Recurrent Neural Network) is used to detect object (here car).

    7. Analysis: Hough transform detects lane, change in lane whereas RNN detects vehicle and system analyze and alert if lane changes.

    8. Alarm: An alarm will alert system when it changes the lane.

  4. IMPLEMENTATION

    1. Dataset Collection:

      The dataset is of Video Format which is converted into frames for processing which contains videos frames of different videos with different conditions

      i.e straight road, curved road, night scene etc.

      Fig.3. Snapshot of Dataset

    2. EXPERIMENTAL RESULTS:

    After applying different algorithms, we have obtained outputs for given system. Canny edge detection and Hough-Transform algorithms have been applied over the dataset.

    Step 1: Load image or video

    Step 2 : Frame Conversion

    The dataset is of Video Format which is converted into frames for processing.

    Fig.4. Snapshot of Frames

    Step 3: Edge Detection of Image

    Algorithm: Canny Edge Detection

    Canny edge detection is a method to take out useful structural information from different vision objects and significantly decrease the amount of data to be processed.

    Output:

    Fig.5: Snapshot of output of edge detection

    Step 4: Region of Interest Segmentation

    After edge detection by canny edge detection algorithm, we can see that the obtained edge not only includes the required lane line edges, but also includes other unnecessary lanes and the edges of the surrounding fences. This method can increase the speed and accuracy of the system.

    Step 5: Lane Detection

    Algorithm: Hough-Transform

    Fig.6: Snapshot of output of lane detection using Hough-transform method

    Step 6: Car Detection (Object Detection) Algorithm: Artificial Neural Network

    Fig.7. Snapshot of Car Detection

    Step 7: Alarm or Warning

    Fig.8. Snapshot of condition when system gives warning after detection of object(car)

  5. PERFORMANCE METRICS, RESULTS & ANALYSIS

    1. Performance Metrics

      As ground truth is not available so we can evaluate the performance metrics of lane detection algorithms by comparing input frames and output frames by calculating true positive(TP), or true negative(TN) or false positive(FP) or false negative(FN).

      • TP is when lane region exists in input frame and it is detected successfully by the model proposed.

      • FP is when method detects the lane roads even when there is no lane in input frame.

      • FN is when there exists a lane region in input frame but method fails to detect.

      • TN is when there is no lane region in input frame and algorithm fails to find it.

      The metrics used to evaluate performance are the standard methods such as precision, recall, accuracy, F score etc.

      Fig 9: Equations to evaluate Performance metrics

    2. Results:

      Following snapshots shows the results of videos named as VIDEO:1(contains only straight road), VIDEO:2(contains curved road), VIDEO:3(contains mixture of straight plus curved road), VIDEO:4(contains curved road) etc.

      Fig.10. Snapshot of result of VIDEO:1

      Fig.11. Snapshot of result of VIDEO:2

      Fig.12. Snapshot of result of VIDEO:3

      Fig.13. Snapshot of result of VIDEO:4

      Following table shows that the results of Hough transform operator which is performed on video dataset which contains video frames of straight and curved roads.

      Table 2: Performance metrics of Video dataset trained using Hough transform

    3. Analysis:

    According to following results straight road gives the better accuracy than curved roads. The accuracy for

    straight road is 96.23% and accuracy for curved road is 90%.

    1. Accuracy:

    2. Recall:

      In information retrieval, recall is the fraction of the relevant documents that are successfully retrieved. It is also called as true positive rate(TPR).

    3. Specificity:

    Specificity measures the proportion of actual negatives that are correctly identified. It is also called the true negative rate(TNR).

    5) False Positive Rate:

    The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events.

    Advantages:

    1. Reduced risk when multiple distractions are present such as when loud children are in the car.

    2. Safer highway driving.

    3. Accident prevention late at night, when fatigue may lead to lane departure.

    4. Improved protection for teen drivers, who have a tendency to drift in the lane.

    5. More warning of accidents when driving in adverse weather conditions.

    6. Compensates for human error when driving.

    Applications:

    1. Reduce unnecessary information in an image while preserving the structure of image.

    2. Extract important features of image like curves, corners, and lines.

    3. Recognizes objects, boundaries and segmentation.

    4. Plays a major role in computer vision and recognition.

    4) Precision:

    In the field of information retrieval, precision is the fraction of retrieved documents that are relevant to the query. It is also called as positive predictive value(PPV).

  6. CONCLUSION

Lane departure warning is an inevitable module in the advanced driver assistance systems. In the last decade several advancements occurred in the lane detection and tracking field. Vision based approach is a very simple modality for detecting lanes. Even though lot of progress has been attained in the lane detection and tracking area, there is still scope for enhancement due to the wide range of variability in the lane environments.

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