scholarly journals (DCNN) Deep Convolution Neural Network Classifier and (EW-CSA) Earth Worm-Crow Search Algorithm for Lane Detection

2019 ◽  
Vol 9 (1) ◽  
pp. 4892-4901
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Lane Detection ◽  
Second Phase ◽  
Vehicle Speed ◽  
Learning Approaches ◽  
The Road ◽  
Optimal Weights ◽  
Car Accidents ◽  
Two Phases

Every year in India, most of the car accidents are occurs and affects on number of lives. Most of the road accidents are occurs due to driver’s inattention and fatigue. Drivers require to focus on different circumstances, together with vehicle speed and path, the separation between vehicles, passing vehicles, and potential risky or uncommon events ahead. Also the accident occurs due to the who bring into play cell phones at the same time as driving, drink and drive, etc. Due to this, most of the companies of automobiles tries to make available best Advanced Driver Assistance System (ADAS) to the customer to avoid the accidents. The lane detection approach is one of the method provided by automobile companies in ADAS, in which the vehicle must follows the lane. Therefore, there is less chance to get an accident. The information obtained from the lane is used to alert the driver. Therefore most of the researchers are attracted towards this field. But, due to the varying road circumstances, it is very difficult to detect the lane. The computer apparition and machine learning approaches are presents in most of the articles. In this article, we presents the deep learning scheme for identification of lane. There are two phases are presents in this work. In a first phase the image transformation is done and in second phase lane detection is occurred. At first, the proposed model gets the numerous lane pictures and changes the picture into its relating Bird's eye view picture by using Inverse perspective mapping transformation. The Deep Convolutional Neural Network (DCNN) classifier to identify the lane from the bird’s eye view image. The Earth Worm- Crow Search Algorithm (EW-CSA) is designed to help DCNN with the optimal weights. The DCNN classifier gets trained with the view picture from the bird’s eye image and the optimal weights are selected through newly developed EW-CSA algorithm. All these algorithms are performed in MATLAB. The simulation results shows that the exact detection of lane of road. Also, the accuracy, sensitivity, and specificity are calculated and its values are 0.99512, 0.9925, and 0.995 respectively.

scholarly journals Multilane Detection Entropy-Based Fusion Model by using Iterative Seed and Optimized Deep Convolutional Neural Network

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1967-1974
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Lane Detection ◽  
Learning Approaches ◽  
Driver Assistance ◽  
Fusion Model ◽  
Driver Assistance Systems ◽  
Proposed Model ◽  
Segmentation Approach ◽  
Deep Convolution Neural Network

In today’s world, the conditions of road is drastically improved as compared with past decade. Most of the express highways are made up of cement concrete and equipped with increased lane size. Apparently speed of the vehicle will increase. Therefore there are more chances for accidents. To avoid the accidents in recent days driver assistance systems are designed to detect the various lane. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. In this paper the entropy based fusion approach is presents for detecting multi-lanes. The Earth Worm- Crow Search Algorithm (EW-CSA) which is based on Deep Convolution Neural Network(DCNN) is utilized for consolidating the outcomes. At first, the deep learning approaches for path location is prepared using an optimization algorithm and EW-CSA, which focus on characterizing every pixel accurately and require post preparing activities to surmise path data. Correspondingly, the region based segmentation approach is utilizing for the multi-lane detection. An entropy based fusion model is used because this method preserved all the information in the image and reduces the noise effects. The performance of proposed model is analyzed in terms of accuracy, sensitivity, and specificity, providing superior results with values 0.991, 0.992, and 0.887, respectively

scholarly journals Multiple Lanes Identification using Novel Region-Based Iterative Seed Method

2019 ◽  
Vol 8 (9S4) ◽  
pp. 171-177
Keyword(s):  
Distance Measure ◽  
Point Of View ◽  
Lane Detection ◽  
Learning Approaches ◽  
Driver Assistance ◽  
The Road ◽  
Performance Point ◽  
Segmented Image ◽  
Car Accidents ◽  
Seed Method

Now a days, in each year thousands of car accidents occurs in India. Therefore, most of the automobile companies tries to give best Advanced Driver Assistance System (ADAS) to avoid the accidents. The lane detection is one of the approach to design the ADAS, if the vehicles follows the lane then there is less chance to get an accident. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. Therefore most of the researchers are attracted towards this field. But, due to the varying road conditions, it is very difficult to detect the lane. The computer vision and machine learning approaches are presents in most of the articles. In this paper, a seed method is designed for the road picture segmentation for the multi-lane detection. The sparking method is applied to the segmented image to increase the speed of computer. In this proposed method, the target grids are selected form the road lane. Distance is calculated for road and lane. Based on the distance measure, the optimal segments are chosen, following an iterative procedure. The accuracy, sensitivity and specificity are considered for the performance point of view for this paper. The calculated maximum detected accuracy is 98.89 %.

scholarly journals Survey Analysis of Robust and Real-Time Multi-Lane and Single Lane Detection in Indian Highway Scenarios

2021 ◽  
Vol 309 ◽  
pp. 01117
Author(s):  
A. Sai Hanuman ◽  
G. Prasanna Kumar
Keyword(s):  
Neural Network ◽  
System Integration ◽  
Large Scale ◽  
Feature Learning ◽  
Semantic Segmentation ◽  
Lane Detection ◽  
Learning Approaches ◽  
Survey Analysis ◽  
Lane Recognition ◽  
Continuous Frames

Studies on lane detection Lane identification methods, integration, and evaluation strategies square measure all examined. The system integration approaches for building a lot of strong detection systems are then evaluated and analyzed, taking into account the inherent limits of camera-based lane detecting systems. Present deep learning approaches to lane detection are inherently CNN's semantic segmentation network the results of the segmentation of the roadways and the segmentation of the lane markers are fused using a fusion method. By manipulating a huge number of frames from a continuous driving environment, we examine lane detection, and we propose a hybrid deep architecture that combines the convolution neural network (CNN) and the continuous neural network (CNN) (RNN). Because of the extensive information background and the high cost of camera equipment, a substantial number of existing results concentrate on vision-based lane recognition systems. Extensive tests on two large-scale datasets show that the planned technique outperforms rivals' lane detection strategies, particularly in challenging settings. A CNN block in particular isolates information from each frame before sending the CNN choices of several continuous frames with time-series qualities to the RNN block for feature learning and lane prediction.

scholarly journals A novel deep neural network for hidden target detection in images

2021 ◽  
Author(s):  
Rabeb Hendaoui ◽  
◽  
Vasif Nabiyev ◽  
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Deep Neural Network ◽  
Target Identification ◽  
Input Image ◽  
Second Phase ◽  
Natural Environments ◽  
Significant Similarity ◽  
Target Segmentation ◽  
Two Phases

The significant similarity between the hidden target and the background makes it difficult to find camouflaged people, such as warriors in warfare, or even camouflaged objects in natural environments. Hence, it is hard to ascertain these concealed targets. To address this issue, a novel deep neural network is proposed in this paper that produces an estimated mask within the hidden target for an input image. Our approach consists of two phases: hidden target segmentation and hidden target identification. For the first phase, we propose the Multilevel Attention Network (MA-Net), which generates the camouflaged target mask based on a Multi-Attention Module (MAM) that helps distinguish the hidden people from the background. Later on, the concealed target will be highlighted in the second phase. Experimental results on the camouflaged people dataset demonstrate that our proposed method can achieve state-of-the-art performance for hidden target detection.

scholarly journals Automatic Academic Paper Rating Based on Convolutional Neural Network

2019 ◽  
pp. 569-574
Author(s):  
Mubeena A. K ◽  
Shahad P.
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Word Embedding ◽  
Second Phase ◽  
Personal Factors ◽  
Rating Score ◽  
Academic Paper ◽  
Proposed Model ◽  
Two Phases ◽  
Source Paper

As an ever increasing number of academic papers are being submitted to journals and conferences, assessing every one of these papers by experts is tedious and can cause imbalance because of the personal factors of the reviewers. In this system, in order to help professionals in assessing academic papers, here propose a task: Automatic Academic Paper Rating (AAPR), which automatically determine whether to accept academic papers. We build a convolutional neural network (CNN) model to achieve automatic academic paper rating task. It has two phases, first phase is identifying abstract part of source paper and generate rating score using CNN model and second phase is taking decision based on the score to accept or decline papers. This model takes word embedding of the abstracts as the input and learns useful features. The word embedding used for training the model is a semantically enriched set of Word2Vec word embedding. After the training phase, the proposed model will be able to generate the score of a new abstract. And find that the title and abstract parts have the most influence on whether the source paper quality when setting aside the other part of source papers. The proposed system outperforms the state-of-art technique.

Motion Planning with Complete Knowledge Using a Colored Som

1997 ◽  
Vol 08 (05n06) ◽  
pp. 613-628 ◽  
Author(s):  
Jules Vleugels ◽  
Joost N. Kok ◽  
Mark Overmars
Keyword(s):  
Neural Network ◽  
Motion Planning ◽  
Configuration Space ◽  
Second Phase ◽  
Planning Problem ◽  
Self Organizing Map ◽  
The Road ◽  
Road Map ◽  
Complete Knowledge ◽  
Colored Version

The motion planning problem requires that a collision-free path be determined for a robot moving amidst a fixed set of obstacles. Most neural network approaches to this problem are for the situation in which only local knowledge about the configuration space is available. The main goal of the paper is to show that neural networks are also suitable tools in situations with complete knowledge of the configuration space. In this paper we present an approach that combines a neural network and deterministic techniques. We define a colored version of Kohonen's self-organizing map that consists of two different classes of nodes. The network is presented with random configurations of the robot and, from this information, it constructs a road map of possible motions in the work space. The map is a growing network, and different nodes are used to approximate boundaries of obstacles and the Voronoi diagram of the obstacles, respectively. In a second phase, the positions of the two kinds of nodes are combined to obtain the road map. In this way a number of typical problems with small obstacles and passages are avoided, and the required number of nodes for a given accuracy is within reasonable limits. This road map is searched to find a motion connecting the given source and goal configurations of the robot. The algorithm is simple and general; the only specific computation that is required is a check for intersection of two polygons. We implemented the algorithm for planar robots allowing both translation and rotation and experiments show that compared to conventional techniques it performs well, even for difficult motion planning scenes.

scholarly journals Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)

2020 ◽  
Vol 10 (7) ◽  
pp. 2543 ◽  
Author(s):  
Jianjun Hu ◽  
Songsong Xiong ◽  
Yuqi Sun ◽  
Junlin Zha ◽  
Chunyun Fu
Keyword(s):  
Region Of Interest ◽  
Global Search ◽  
Lane Detection ◽  
Vehicle Speed ◽  
Feature Points ◽  
The Road ◽  
Detection Approach ◽  
Polar Coordinate ◽  
Previous Frame ◽  
Lane Line

A novel lane detection approach, based on the dynamic region of interest (DROI) selection in the horizontal and vertical safety vision, is proposed to improve the accuracy of lane detection in this paper. The curvature of each point on the edge of the road and the maximum safe distance, which are solved by the lane line equation and vehicle speed data of the previous frame, are used to accurately select the DROI at the current moment. Next, the global search of DROI is applied to identify the lane line feature points. Subsequently, the discontinuous points are processed by interpolation. To fulfill fast and accurate matching of lane feature points and mathematical equations, the lane line is fitted in the polar coordinate equation. The proposed approach was verified by the Caltech database, under the premise of ensuring real-time performance. The accuracy rate was 99.21% which is superior to other mainstream methods described in the literature. Furthermore, to test the robustness of the proposed method, it was tested in 5683 frames of complicated real road pictures, and the positive detection rate was 99.07%.

scholarly journals Multiple Lanes Identification for Advanced Driver Assistance System (ADAS)

2019 ◽  
Vol 8 (5S3) ◽  
pp. 533-538
Keyword(s):  
Lane Detection ◽  
Detection Methods ◽  
Learning Approaches ◽  
Drowsy Driving ◽  
The Road ◽  
Perspective Distortion ◽  
Lane Departure ◽  
Lane Recognition ◽  
Feature Based ◽  
Straight Lines

Now a days, a multi-lane recognition technique that uses the ridge features and the inverse perspective mapping (IPM) is generally used to distinguish lanes since it can evacuate the perspective distortion on lines that lie in parallel in reality. The lane detection is one of the approach to design the ADAS, if the vehicles follows the lane then there is less chance to get an accident. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. Therefore most of the researchers are attracted towards this field. But, due to the varying road conditions, it is very difficult to detect the lane. The computer vision and machine learning approaches are presents in most of the articles. In this paper, a survey of different method is presents for the road picture segmentation for the multi-lane detection. The Lane Departure Warning (LDW) system can help to reduce vehicle crashes that are caused by careless or drowsy driving. There has been much research on vision based lane detection for the LDW system. In these lane detection methods, color or edge information is utilized as a feature of the lane. The feature-based methods are usually applied to localize the lanes in the road images by extracting low-level features. On the other hand, the model-based methods use several geometrical elements to describe the lanes, including parabolic curves, hyperbola and straight lines. Feature-based methods require a dataset containing several thousand images of the roads with well-painted and prominent lane markings that are subsequently converted to features. Moreover, these methods may suffer from noise.

scholarly journals Lane Detection Method with Impulse Radio Ultra-Wideband Radar and Metal Lane Reflectors

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 324 ◽  
Author(s):  
Dae-Hyun Kim
Keyword(s):  
Detection Method ◽  
Impulse Radio ◽  
Lane Detection ◽  
Ultra Wideband ◽  
Vehicle Speed ◽  
Range Resolution ◽  
The Road ◽  
Detection Technology ◽  
On The Road ◽  
Wideband Radar

An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely depending on the lighting conditions. Consequently, many studies have focused on using radar for lane detection. However, when using radar, it is difficult to distinguish between the plain road surface and painted lane markers, necessitating the use of radar reflectors for guidance. Previous studies have used long-range radars which may receive interference signals from various objects, including other vehicles, pedestrians, and buildings, thereby hampering lane detection. Therefore, we propose a lane detection method that uses an impulse radio ultra-wideband radar with high-range resolution and metal lane markers installed at regular intervals on the road. Lane detection and departure is realized upon using the periodically reflected signals as well as vehicle speed data as inputs. For verification, a field test was conducted by attaching radar to a vehicle and installing metal lane markers on the road. Experimental scenarios were established by varying the position and movement of the vehicle, and it was demonstrated that the proposed method enables lane detection based on the data measured.

A New Road-Side Array-Based Method for Characterization of Truck Noise During Passby

2008 ◽  
Author(s):  
William K. Blake ◽  
Paul Donovan
Keyword(s):  
Wind Tunnels ◽  
Vehicle Speed ◽  
California Department ◽  
Test Track ◽  
The Road ◽  
State Highway ◽  
Two Phases ◽  
Definition Of ◽  
Acoustic Calibration

This paper describes the development of a new method for measuring pass-by sound from trucks and other vehicles using 2-dimensional arrays. The approach provides 2-dimensional quantitative maps “images” of the cross-range and elevation distribution in the vehicle side view. The method is an application and extension of an array technology that was originally used for the characterization of static aeroacoustic sources in wind tunnels. The focus of this work is on identifying and rank-ordering the important contributing sources of passby noise. This development includes two phases: developmental testing at a test track site, and road-side testing at two California State highway sites. The acquisition post-processing allows the “observer” to track the vehicle cross-range in order to create a time sequence of source maps that may be interpreted as both level relationships and directivity patterns. The processing applies both range and approximate Doppler adjustments to spectra as a function of time during pass-by or, equivalently, to vehicle position relative to the array’s center. An image demodulation scheme is shown to clarify the images. The initial phase of this work occurred at a test track using known “cooperative” truck sources. This experience permitted the verification of the method and the definition of a final measurement approach that was viable at a highway site. Subjects were all trucks that varied in model, vehicle speed, tread, and the presence of a trailer. The array beamformer’s ability to localize and the measurement system’s ability to track were validated using both stationary and moving sources. Following validation at the test track site, the instrumentation was transferred to two California highway sites. There, acoustic calibration was used to align the array with the road track and to provide a spatial reference for mapping the “images”. Both light and heavy vehicles at these sites were “uncooperative” with arrivals and speeds randomly determined by traffic flow. This work was funded by the California Department of Transportation.

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