scholarly journals Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5051
Author(s):  
Rujin Ma ◽  
Zhen Zhang ◽  
Yiqing Dong ◽  
Yue Pan
Keyword(s):  
Neural Network ◽  
Bridge Deck ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Strain Sensors ◽  
Long Span ◽  
Long Span Bridge ◽  
Pavement Evaluation ◽  
Close Range

Vehicle detection and classification have become important tasks for traffic monitoring, transportation management and pavement evaluation. Nowadays there are sensors to detect and classify the vehicles on road. However, on one hand, most sensors rely on direct contact measurement to detect the vehicles, which have to interrupt the traffic. On the other hand, complex road scenes produce much noise to consider when to process the signals. In this paper, a data-driven methodology for the detection and classification of vehicles using strain data is proposed. The sensors are well arranged under the bridge deck without traffic interruption. Next, a cascade pre-processing method is applied for vehicle detection to eliminate in-situ noise. Then, a neural network model is trained to identify the close-range following vehicles and separate them by Non-Maximum Suppression. Finally, a deep convolutional neural network is designed and trained to identify the vehicle types based on the axle group. The methodology was applied in a long-span bridge. Three strain sensors were installed beneath the bridge deck for a week. High robustness and accuracy were obtained by these algorithms. The methodology proposed in this paper is an adaptive and promising method for vehicle detection and classification under complex noise. It would serve as a supplement to current transportation systems and provide reliable data for management and decision-making.

scholarly journals The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Liwen Zhang ◽  
Chao Zhang ◽  
Zhuo Sun ◽  
You Dong ◽  
Pu Wei
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Long Span ◽  
Long Span Bridge ◽  
Vibration Responses ◽  
Bp Neural Network Model

The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

scholarly journals The One-Stage Detector Algorithm Based on Background Prediction and Group Normalization for Vehicle Detection

2020 ◽  
Vol 10 (17) ◽  
pp. 5883
Author(s):  
Fei Lu ◽  
Fei Xie ◽  
Shibin Shen ◽  
Jiquan Yang ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Network Models ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Detector Performance ◽  
One Stage ◽  
Complex Scenes ◽  
The One

Vehicle detection in intelligent transportation systems (ITS) is a very important and challenging task in traffic monitoring. The difficulty of this task is to accurately locate and classify relatively small vehicles in complex scenes. To solve these problems, this paper proposes a modified one-stage detector based on background prediction and group normalization to realize real-time and accurate detection of traffic vehicles. The one-stage detector firstly adds a module to adjust the width and height of anchors and predict the target background, which avoids the problem of the target vehicle missing detection or wrong detection due to the influence of the complicated environments. Then, group normalization and the loss function based on weight attenuation can improve the one-stage detector performance in the training process. The experimental results on traffic monitoring datasets indicate that the improved one-stage detector is superior to the other neural network models in terms of precision at 95.78%.

scholarly journals Efficient Deep Network Architecture for Vision-Based Vehicle Detection Keyvan Kasiri,

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Keyvan Kasiri ◽  
Mohammad Javad Shafiee ◽  
Francis Li ◽  
Alexander Wong ◽  
Justin Eichel
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Surveillance Systems ◽  
Performance Accuracy ◽  
Sparse Set

With the progress in intelligent transportation systems in smartcities, vision-based vehicle detection is becoming an important issuein the vision-based surveillance systems. With the advent ofthe big data era, deep learning methods have been increasinglyemployed in the detection, classification, and recognition applicationsdue to their performance accuracy, however, there are stillmajor concerns regarding deployment of such methods in embeddedapplications. This paper offers an efficient process leveragingthe idea of evolutionary deep intelligence on a state-of-the-art deepneural network. Using this approach, the deep neural network isevolved towards a highly sparse set of synaptic weights and clusters.Experimental results for the task of vehicle detection demonstratethat the evolved deep neural network can achieve a substantialimprovement in architecture efficiency adapting for GPUacceleratedapplications without significant sacrifices in detectionaccuracy. The architectural efficiency of ~4X-fold and ~2X-folddecrease is obtained in synaptic weights and clusters, respectively,while the accuracy of 92.8% (drop of less than 4% compared to theoriginal network model) is achieved. Detection results and networkefficiency for the vehicular application are promising, and opensthe door to a wider range of applications in deep learning.

scholarly journals Steady-Suction-Based Flow Control of Flutter of Long-Span Bridge

2020 ◽  
Vol 10 (4) ◽  
pp. 1372
Author(s):  
Jian Zhan ◽  
Hongfu Zhang ◽  
Zhiwen Liu ◽  
Huan Liu ◽  
Dabo Xin ◽  
...  
Keyword(s):  
Flow Control ◽  
Bridge Deck ◽  
Control Method ◽  
Control Device ◽  
Maximal Increase ◽  
Long Span ◽  
Suction Control ◽  
Long Span Bridge ◽  
Bridge Model ◽  
Section Model

The present wind tunnel study focuses on the effects of the steady-suction-based flow control method on the flutter performance of a 2DOF bridge deck section model. The suction applied to the bridge model was released from slots located at the girder bottom. The suction rates of all slots along the span were equal and constant. A series of test cases with different combinations of suction slot positions, suction intervals, and suction rates were studied in detail for the bridge deck model. The experimental results showed that the steady-suction-based flow control method could improve the flutter characteristics of the bridge deck with a maximal increase in the critical flutter speed of up to 10.5%. In addition, the flutter derivatives (FDs) of the bridge deck with or without control were compared to investigate the fundamental mechanisms of the steady-suction-based control method. According to the results, installing a suction control device helps to strengthen aerodynamic damping, which is the primary cause for enhanced flutter performance of bridge decks.

scholarly journals Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Yang ◽  
Lin Yang ◽  
Bo Wu ◽  
Gang Yao ◽  
Hang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Traffic Control ◽  
Evaluation Model ◽  
Wind Tunnel Test ◽  
Safety Evaluation ◽  
Batch Size ◽  
Vehicle Safety ◽  
Long Span ◽  
Long Span Bridge ◽  
Fully Connected

The safety condition of vehicles passing on long-span bridges has attracted more and more attention in recent years. Many research studies have been done to find convenience and efficiency measures. A vehicle safety evaluation model passing on a long-span bridge is presented in this paper based on fully connected neural network (FCN). The first step is to investigate the long-span bridge responses with wind excitation by using the wind tunnel test and finite element model. Subsequently, typical vehicle models are given and a vehicle-bridge system is established by considering weather conditions. Accident types of vehicles with severe weather are estimated. In particular, the input and output variables of the vehicle safety evaluation model are determined, and simultaneously training, validation, and testing data are achieved. Twenty-nine models have been compared and analyzed by using hidden layer, initial learning rate, batch size, activation function, and optimization method. It is found that the 4-15-15-4 model occupies a preferable prediction performance, and it can provide a kind of utility for traffic control and reduce the probability of vehicle accidents on the bridge.

scholarly journals FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7399
Author(s):  
Ming-Hwa Sheu ◽  
S M Salahuddin Morsalin ◽  
Jia-Xiang Zheng ◽  
Shih-Chang Hsia ◽  
Cheng-Jian Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Sigmoid Function ◽  
Real Time Processing ◽  
Scale Choice ◽  
Training Stage

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.

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