scholarly journals Road Lane Marking Detection with Deep Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 708-718
Author(s):  
Manu S
Keyword(s):  
Neural Network ◽  
Significant Variation ◽  
External Environment ◽  
Weather Condition ◽  
Lane Detection ◽  
Color Intensity ◽  
Detection Systems ◽  
Lane Marking ◽  
Detection Technology ◽  
Segmentation Problem

Road Lane detection is an important factor for Advanced Driver Assistant System (ADAS). In this paper, we propose a lane detection technology using deep convolutional neural network to extract lane marking features. Many conventional approaches detect the lane using the information of edge, color, intensity and shape. In addition, lane detection can be viewed as an image segmentation problem. However, most methods are sensitive to weather condition and noises; and thus, many traditional lane detection systems fail when the external environment has significant variation.

The Application of BP Neural Network in Network Intrusion Detection

2013 ◽  
Vol 765-767 ◽  
pp. 1415-1418 ◽  
Author(s):  
Ya Fang Lou ◽  
Zhi Jun Yuan ◽  
Hao Wu
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Practical Significance ◽  
Network Intrusion Detection ◽  
Critical Problem ◽  
Detection Model ◽  
Detection Systems ◽  
Network Intrusion ◽  
Detection Technology

As the network is impacting enormously to all aspects of society, the network security becomes a critical problem. The traditional intrusion detection technology exists some disadvantages: the imperfection of architecture, the slow detecting of system, the vulnerable of itself architecture, and so on. This paper presents an intrusion detection model based on BP neural network which has the incomparable advantages against traditional intrusion detection systems. Therefore, the study of this subject possesses the practical significance.

scholarly journals Intrusion Detection System for NSL-KDD Dataset Based on Deep Learning and Recursive Feature Elimination

2021 ◽  
Vol 39 (7) ◽  
pp. 1069-1079
Author(s):  
Bilal Mohammed ◽  
Ekhlas K. Gbashi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Binary Classification ◽  
Recursive Feature Elimination ◽  
Suggested Model ◽  
Accuracy Rate ◽  
Detection Systems ◽  
Detection Technology

Intrusion detection system is responsible for monitoring the systems and detect attacks, whether on (host or on a network) and identifying attacks that could come to the system and cause damage to them, that’s mean an IDS prevents unauthorized access to systems by giving an alert to the administrator before causing any serious harm. As a reasonable supplement of the firewall, intrusion detection technology can assist systems to deal with offensive, the Intrusions Detection Systems (IDSs) suffers from high false positive which leads to highly bad accuracy rate. So this work is suggested to implement (IDS) by using a Recursive Feature Elimination to select features and use Deep Neural Network (DNN) and Recurrent Neural Network (RNN) for classification, the suggested model gives good results with high accuracy rate reaching 94%, DNN was used in the binary classification to classify either attack or Normal, while RNN was used in the classifications for the five classes (Normal, Dos, Probe, R2L, U2R). The system was implemented by using (NSL-KDD) dataset, which was very efficient for offline analyses systems for IDS.                                                                                                   

Lane marking detection and classification with combined deep neural network for driver assistance

2018 ◽  
Vol 233 (5) ◽  
pp. 1259-1268 ◽  
Author(s):  
Weiwei Zhang ◽  
Hui Liu ◽  
Xuncheng Wu ◽  
Lingyun Xiao ◽  
Yubin Qian ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Lane Detection ◽  
Convolution Neural Network ◽  
Detection Accuracy ◽  
Lane Marking ◽  
Fitting Method ◽  
Boundary Fitting ◽  
The Time Domain ◽  
Continuous Frames

An efficient approach for lane marking detection and classification by the combination of convolution neural network and recurrent neural network is proposed in this paper. First, convolution neural network is trained for lane marking features extraction, and then these convolution neural network features of continuous frames are transferred to recurrent neural network model for lane boundary detection and classification in the time domain. At last, a lane boundary fitting method based on dynamic programming is proposed to improve the lane detection accuracy and robustness. The method presented generates satisfactory results of lane detection and type classification under various traffic conditions according to the experimental results, which show that the approach provided in this paper outperforms traditional methods and the total lane markings classification reached 95.21% accuracy.

scholarly journals Neural Network for Metal Detection Based on Magnetic Impedance Sensor

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4456
Author(s):  
Sungjae Ha ◽  
Dongwoo Lee ◽  
Hoijun Kim ◽  
Soonchul Kwon ◽  
EungJo Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Deep Learning ◽  
Short Term Memory ◽  
Magnetic Impedance ◽  
Detection Technology ◽  
Metal Detection ◽  
Long Short Term Memory ◽  
Impedance Sensor ◽  
The Magnetic Field

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.

Risk of Voltage Collapse Using Multi-Layer Feed-Forward Neural Network and Generalized Regression Neural Network

2015 ◽  
Vol 793 ◽  
pp. 483-488
Author(s):  
N. Aminudin ◽  
Marayati Marsadek ◽  
N.M. Ramli ◽  
T.K.A. Rahman ◽  
N.M.M. Razali ◽  
...  
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Risk Index ◽  
Weather Condition ◽  
Generalized Regression Neural Network ◽  
Voltage Collapse ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Generalized Regression

The computation of security risk index in identifying the system’s condition is one of the major concerns in power system analysis. Traditional method of this assessment is highly time consuming and infeasible for direct on-line implementation. Thus, this paper presents the application of Multi-Layer Feed Forward Network (MLFFN) to perform the prediction of voltage collapse risk index due to the line outage occurrence. The proposed ANN model consider load at the load buses as well as weather condition at the transmission lines as the input. In realizing the effectiveness of the proposed method, the results are compared with Generalized Regression Neural Network (GRNN) method. The results revealed that the MLFFN method shows a significant improvement over GRNN performance in terms of least error produced.

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.

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