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 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 Calibrating Network Traffic with One-Dimensional Convolutional Neural Network with Autoencoder and Independent Recurrent Neural Network for Mobile Malware Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Songjie Wei ◽  
Zedong Zhang ◽  
Shasha Li ◽  
Pengfei Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Network Traffic ◽  
Malware Detection ◽  
Smart Devices ◽  
Detection Accuracy ◽  
One Dimensional ◽  
Mobile Malware ◽  
Mobile Malware Detection

In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. The design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconstruct and apply the one-dimensional convolutional neural network to extract local features from multiple network flows. The autoencoder is applied to digest the principal traffic features from the neural network and is integrated into the independent recurrent neural network construction to highlight the sequential relationship between the highly significant features. In addition, the Softmax function with the LReLU activation function is adjusted and embedded to the neurons of the independent recurrent neural network to effectively alleviate the problem of unstable training. We conduct a series of experiments to evaluate the effectiveness of the proposed method and its performance for the 1DCAE-IndRNN-integrated detection procedure. The detection results of the public Android malware dataset CICAndMal2017 show that the proposed method achieves up to 98% detection accuracy and recall rates with clear advantages over other benchmark methods.

scholarly journals Design of Secure Blockchain Convolution Neural Network Architecture for Detection Malware Attacks

2020 ◽  
Vol 8 (6) ◽  
pp. 3055-3060
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Information Privacy ◽  
Convolution Neural Network ◽  
Detection Accuracy ◽  
Neural Network Architecture ◽  
Network Method ◽  
Advance Method ◽  
Learning Concept

Nowadays, Cyberattack continues to target the applications and networks more than past with different and advance ways like programming complex format of malware that it executes unauthorized action on the targeted system, so it is needed to develop and deploy advance method to these kind of attacks for detecting correctly with a trusted and a better accuracy. Therefore, the recent solutions to detect malware attacks focuses on new advance technologies like Deep learning and Machine learning concepts. In this paper we have developed secure blockchain convolution (SBC) Algorithm that provides a better way of analyzing malware data with effectiveness and accuracy. The deep learning concept does not involve in a method to identify the trust while the process is led to extraction of the features as it can be infected by the intervention of human or a trained system. Therefore, According to research which is done towards blockchain, it features as authentication function, immutable property, information privacy and safety helps in deployment of Convolution Neural Network method with better detection. Blockchain has a decentralized structure which is able to record the data between various parties and it helps in preventing the manipulation when the deep learning concept is applied and the higher detection accuracy is received in the limited time.

scholarly journals Convolutional Recurrent Neural Network-Based Event Detection in Tunnels Using Multiple Microphones

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2695 ◽  
Author(s):  
Nam Kyun Kim ◽  
Kwang Myung Jeon ◽  
Hong Kook Kim
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Background Noise ◽  
Source Separation ◽  
Error Rates ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Noisy Signal ◽  
Recognition Error

This paper proposes a sound event detection (SED) method in tunnels to prevent further uncontrollable accidents. Tunnel accidents are accompanied by crashes and tire skids, which usually produce abnormal sounds. Since the tunnel environment always has a severe level of noise, the detection accuracy can be greatly reduced in the existing methods. To deal with the noise issue in the tunnel environment, the proposed method involves the preprocessing of tunnel acoustic signals and a classifier for detecting acoustic events in tunnels. For preprocessing, a non-negative tensor factorization (NTF) technique is used to separate the acoustic event signal from the noisy signal in the tunnel. In particular, the NTF technique developed in this paper consists of source separation and online noise learning. In other words, the noise basis is adapted by an online noise learning technique for enhancement in adverse noise conditions. Next, a convolutional recurrent neural network (CRNN) is extended to accommodate the contributions of the separated event signal and noise to the event detection; thus, the proposed CRNN is composed of event convolution layers and noise convolution layers in parallel followed by recurrent layers and the output layer. Here, a set of mel-filterbank feature parameters is used as the input features. Evaluations of the proposed method are conducted on two datasets: a publicly available road audio events dataset and a tunnel audio dataset recorded in a real traffic tunnel for six months. In the first evaluation where the background noise is low, the proposed CRNN-based SED method with online noise learning reduces the relative recognition error rate by 56.25% when compared to the conventional CRNN-based method with noise. In the second evaluation, where the tunnel background noise is more severe than in the first evaluation, the proposed CRNN-based SED method yields superior performance when compared to the conventional methods. In particular, it is shown that among all of the compared methods, the proposed method with the online noise learning provides the best recognition rate of 91.07% and reduces the recognition error rates by 47.40% and 28.56% when compared to the Gaussian mixture model (GMM)–hidden Markov model (HMM)-based and conventional CRNN-based SED methods, respectively. The computational complexity measurements also show that the proposed CRNN-based SED method requires a processing time of 599 ms for both the NTF-based source separation with online noise learning and CRNN classification when the tunnel noisy signal is one second long, which implies that the proposed method detects events in real-time.

scholarly journals Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

2019 ◽  
Vol 11 (21) ◽  
pp. 2483 ◽  
Author(s):  
Zhang ◽  
Zhang ◽  
Shi ◽  
Wei
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
High Speed ◽  
Fishery Management ◽  
Convolution Neural Network ◽  
Detection Accuracy ◽  
Ship Detection ◽  
Military Planning ◽  
Detection Speed

As an active microwave imaging sensor for the high-resolution earth observation, synthetic aperture radar (SAR) has been extensively applied in military, agriculture, geology, ecology, oceanography, etc., due to its prominent advantages of all-weather and all-time working capacity. Especially, in the marine field, SAR can provide numerous high-quality services for fishery management, traffic control, sea-ice monitoring, marine environmental protection, etc. Among them, ship detection in SAR images has attracted more and more attention on account of the urgent requirements of maritime rescue and military strategy formulation. Nowadays, most researches are focusing on improving the ship detection accuracy, while the detection speed is frequently neglected, regardless of traditional feature extraction methods or modern deep learning (DL) methods. However, the high-speed SAR ship detection is of great practical value, because it can provide real-time maritime disaster rescue and emergency military planning. Therefore, in order to address this problem, we proposed a novel high-speed SAR ship detection approach by mainly using depthwise separable convolution neural network (DS-CNN). In this approach, we integrated multi-scale detection mechanism, concatenation mechanism and anchor box mechanism to establish a brand-new light-weight network architecture for the high-speed SAR ship detection. We used DS-CNN, which consists of a depthwise convolution (D-Conv2D) and a pointwise convolution (P-Conv2D), to substitute for the conventional convolution neural network (C-CNN). In this way, the number of network parameters gets obviously decreased, and the ship detection speed gets dramatically improved. We experimented on an open SAR ship detection dataset (SSDD) to validate the correctness and feasibility of the proposed method. To verify the strong migration capacity of our method, we also carried out actual ship detection on a wide-region large-size Sentinel-1 SAR image. Ultimately, under the same hardware platform with NVIDIA RTX2080Ti GPU, the experimental results indicated that the ship detection speed of our proposed method is faster than other methods, meanwhile the detection accuracy is only lightly sacrificed compared with the state-of-art object detectors. Our method has great application value in real-time maritime disaster rescue and emergency military planning.

Fault Detection Method of Power Insulator Based on Deep Convolution Neural Network

2021 ◽  
Author(s):  
Yan Wang ◽  
Weijie Zhang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Detection Method ◽  
Convolution Neural Network ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Region Selection ◽  
Function Design ◽  
Deep Convolution Neural Network ◽  
Anchor Design

Aiming at the problem of low detection accuracy of traditional power insulator fault detection methods, a power insulator fault detection method based on deep convolution neural network is designed. For the training of deep convolution neural network, the fault detection of power insulator based on deep convolution neural network is realized by anchor design, loss function design, candidate region selection mechanism establishment and sharing convolution features. The experimental results show that the fault detection method of power insulator based on deep convolution neural network is more accurate than the traditional method, and the detection time is less.

scholarly journals Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

2021 ◽  
Vol 16 (59) ◽  
pp. 461-470
Author(s):  
Thanh Bui-Tien ◽  
Dung Bui-Ngoc ◽  
Hieu Nguyen-Tran ◽  
Lan Nguyen-Ngoc ◽  
Hoa Tran-Ngoc ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Recurrent Neural Network ◽  
Health Monitoring ◽  
Damage Identification ◽  
Convolution Neural Network ◽  
Series Data ◽  
Structural Health

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure.

scholarly journals Deteksi Arteri Karotis pada Citra Ultrasound B-Mode Berbasis Convolution Neural Network Single Shot Multibox Detector

2019 ◽  
Vol 7 (2) ◽  
pp. 56-63
Author(s):  
I Made Gede Sunarya ◽  
Tita Karlita ◽  
Joko Priambodo ◽  
Rika Rokhana ◽  
Eko Mulyanto Yuniarno ◽  
...  
Keyword(s):  
Neural Network ◽  
Carotid Artery ◽  
Convolution Neural Network ◽  
Primary Data ◽  
Process Time ◽  
Detection Accuracy ◽  
Ultrasound Images ◽  
Single Shot ◽  
Real Time System ◽  
Median Filters

Detection of vascular areas (blood vessels) using B-Mode ultrasound images is needed for automatic applications such as registration and navigation in medical operations. This study developed the detection of the carotid artery area using Convolution Neural Network Single Shot Network Multibox Detector (SSD) to determine the bounding box ROI of the carotid artery area in B-mode ultrasound images. The data used are B-Mode ultrasound images on the neck that contain the carotid artery area (primary data). SSD method result is 95% of accuracy which is higher than the Hough transformation method, Ellipse method, and Faster RCNN in detecting carotid artery area in the B-Mode ultrasound image. The use of image enhancement with Gaussian filter, histogram equalization, and Median filters in this method can increase detection accuracy. The best process time of the proposed method is 2.09 seconds so that it can be applied in a real-time system.

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