scholarly journals Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing

2017 ◽  
Vol 2017 ◽  
pp. 1-21 ◽  
Author(s):  
Jiandong Zhao ◽  
Hongqiang Wu ◽  
Liangliang Chen
Keyword(s):  
Image Segmentation ◽  
Surface State ◽  
Traffic Accidents ◽  
Surface Condition ◽  
Surface States ◽  
Road Surface ◽  
Support Vector ◽  
Image Size ◽  
State Recognition ◽  
Penalty Factor

Adverse road condition is the main cause of traffic accidents. Road surface condition recognition based on video image has become a central issue. However, hybrid road surface and road surface under different lighting environments are two crucial problems. In this paper, the road surface states are categorized into 5 types including dry, wet, snow, ice, and water. Then, according to the original image size, images are segmented; 9-dimensional color eigenvectors and 4 texture eigenvectors are extracted to construct road surface state characteristics database. Next, a recognition method of road surface state based on SVM (Support Vector Machine) is proposed. In order to improve the recognition accuracy and the universality, a grid searching algorithm and PSO (Particle Swarm Optimization) algorithm are used to optimize the kernel function factor and penalty factor of SVM. Finally, a large number of actual road surface images in different environments are tested. The results show that the method based on SVM and image segmentation is feasible. The accuracy of PSO algorithm is more than 90%, which effectively solves the problem of road surface state recognition under the condition of hybrid or different video scenes.

scholarly journals Pavement State Recognition Method Considering Slope

2021 ◽  
Vol 2113 (1) ◽  
pp. 012080
Author(s):  
Xiuhao Xi ◽  
Jun Xiao ◽  
Qiang Zhang ◽  
Yanchao Wang
Keyword(s):  
Surface Condition ◽  
Lateral Force ◽  
Longitudinal Force ◽  
Operating Conditions ◽  
Road Surface ◽  
Tire Model ◽  
State Recognition ◽  
Adhesion Coefficient ◽  
The Road ◽  
Real Time Tracking

Abstract For the problem of road surface condition recognition, this paper proposes a real-time tracking method to estimate road surface slope and adhesion coefficient. Based on the fusion of dynamics and kinematics, the current road slope of the vehicle which correct vertical load is estimated. The effect of the noise from dynamic and kinematic methods on the estimation results is removed by designing a filter. The normalized longitudinal force and lateral force are calculated by Dugoff tire model, and the Jacobian matrix of the vector function of the process equation is obtained by combining the relevant theory of EKF algorithm. The road adhesion coefficient is estimated finally. The effectiveness of the algorithm is demonstrated by analyzing the results under different operating conditions, such as docking road and bisectional road, using a joint simulation of Matlab/Simulink and Carsim.

Researches concerning the Aerodynamic Sorting of Solid Particles According to the Surface States

2008 ◽  
Vol 59 (3) ◽  
pp. 360-365 ◽  
Author(s):  
Valentin Nedeff ◽  
Emilian Mosnegutu ◽  
Mirela Panainte ◽  
Carment Savin ◽  
Bogdan Macarescu
Keyword(s):  
Binary Mixture ◽  
Rough Surface ◽  
Surface State ◽  
Smooth Surface ◽  
Surface Condition ◽  
Surface States ◽  
Aerodynamic Characteristics ◽  
Solid Particles ◽  
Separation Degree ◽  
Sorting Process

The process of aerodynamic sorting of a solid particles mixture is influenced by the aerodynamic characteristics of the particles (shape, dimensions, the surface state and the density of the particle), which determine the behaviour of particles in the air flows. In this study it was analyzed the influence of the surface state of solid particles on the separation degree of a binary mixture, made from particles with smooth surface and particles with rough surface, submitted to the aerodynamic sorting. The correlation between the surface state of solid particles and their behavior in air flow is emphasized by the weight mass of the particles deposited in the collection boxes. It was observed that the particles with the smooth surface have higher floating velocities than the particles with the rough surface for closed dimensions, shapes and densities. The sorting process is influenced by the surface condition of the particle.

Surface Condition in the Plasma-CVD of a-Si:H,F from SiF4 and H2

1988 ◽  
Vol 131 ◽  
Author(s):  
A. Maruyama ◽  
D. S. Shen ◽  
V. Chu ◽  
J. Z. Liu ◽  
J. Jaroker ◽  
...  
Keyword(s):  
Surface State ◽  
Growth Temperature ◽  
Urbach Energy ◽  
Film Growth ◽  
Surface Condition ◽  
Surface States ◽  
State Density ◽  
Growth Surface ◽  
Plasma Cvd ◽  
Band Tail

ABSTRACTWe present a detailed study of the growth of a-Si:H,F from SiF4 and H4. The growth surface appears to have a high density of surface states. These surface states can be thermally relaxed by keeping the films at growth temperature after the termination of growth, suggesting that the states were created during film growth. When frozen in, the surface state density is found to depend on the conditions during film growth. The density is related to the sharpness of the valence band tail as measured by the Urbach Energy. We believe that a reaction on the growth surface resulting in fluorine elimination creates these surface states and also affects the formation of the Si-network.

Distinction of Wet Road Surface Condition at Night-time using Texture Features

2012 ◽  
Vol 132 (9) ◽  
pp. 1488-1493 ◽  
Author(s):  
Keiji Shibata ◽  
Tatsuya Furukane ◽  
Shohei Kawai ◽  
Yuukou Horita
Keyword(s):  
Surface Condition ◽  
Texture Features ◽  
Road Surface ◽  
Night Time

scholarly journals Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling

2021 ◽  
Vol 18 (11) ◽  
pp. 5604
Author(s):  
Yookyung Boo ◽  
Youngjin Choi
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
National Level ◽  
Brier Score ◽  
Support Vector ◽  
Main Diagnosis ◽  
Road Traffic Accidents ◽  
Multiple Variables ◽  
Type Of Injury ◽  
Kernel Model

In this study, four models—logistic regression (LR), random forest (RF), linear support vector machine (SVM), and radial basis function (RBF)-SVM—were compared for their accuracy in determining mortality caused by road traffic injuries. They were tested using five years of national-level data from the Korea Disease Control and Prevention Agency’s (KDCA) National Hospital Discharge In-Depth Survey (2013 through to 2017). Model performance was measured for accuracy, precision, recall, F1 score, and Brier score metrics using classification analysis that included characteristics of patients, accidents, injuries, and illnesses. Due to the number of variables and differing units, the rates of survival and mortality related to road traffic accidents were imbalanced, so the data was corrected and standardized before the classification models’ performances were compared. Using the importance analysis, the main diagnosis, the type of injury, the site of the injury, the type of injury, the operation status, the type of accident, the role at the time of the accident, and the sex were selected as the analysis factors. The biggest contributing factor was the role in the accident, which is the driver, and the major sites of the injuries were head injuries and deep injuries. Using selected factors, comparisons of the classification performance of each model indicated RBF-SVM and RF models were superior to the others. Of the SVM models, the RBF kernel model was superior to the linear kernel model; it can be inferred that the performance of the high-dimensional transformed RBF model is superior when the dimension is complex because of the use of multiple variables. The findings suggest there are limitations to analyses involving imbalanced, multidimensional original data, such as data on road traffic mortality. Thus, analyses must be performed after imbalances are corrected.

scholarly journals A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4893 ◽  
Author(s):  
Hejar Shahabi ◽  
Ben Jarihani ◽  
Sepideh Tavakkoli Piralilou ◽  
David Chittleborough ◽  
Mohammadtaghi Avand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Vegetation Index ◽  
Scale Parameter ◽  
Slope Aspect ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Slope Length ◽  
Optimal Scale ◽  
Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

Multi-View Vehicle Recognition Based on WRT-SVM

2013 ◽  
Vol 694-697 ◽  
pp. 1987-1992 ◽  
Author(s):  
Xing Gang Wu ◽  
Cong Guo
Keyword(s):  
False Positive Rate ◽  
Identification Problem ◽  
Classification Problem ◽  
Random Trees ◽  
Support Vector ◽  
Image Size ◽  
Scale Invariant ◽  
Vehicle Recognition ◽  
Positive Rate ◽  
Image Pairs

Proposed an approach to identify vehicles considering the variation in image size, illumination, and view angles under different cameras using Support Vector Machine with weighted random trees (WRT-SVM). With quantizing the scale-invariant features of image pairs by the weighted random trees, the identification problem is formulated as a same-different classification problem. Results show the efficiency of building the randomized tree due to the weights of the samples and the control of the false-positive rate of the identify system.

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