Image Processing‐Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

Hoang, Nhat‐Duc

doi:10.1155/2020/8829715

Cited by 18 publications

(11 citation statements)

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“…erefore, image texture analysis used for extracting the coarseness of image regions is helpful to recognize them. Texture descriptors [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] have been proved to be highly useful for image classification in various fields. In this study, the highly discriminative local ternary pattern is employed.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.

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Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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“…Santos et al [ 84 ] proposed an automatic identification method for concrete spalling in reinforced concrete bridges based on AlexNet migration learning with an accuracy of 99.1%. Hoang et al [ 85 ] used a Gabor filter to extract texture information of concrete spalling and a logistic regression model based on the state-of-the-art adaptive moment estimation to detect concrete spalling. However, the method was unable to detect minor spalling.…”

Section: Cv-based Surface Defect Detectionmentioning

confidence: 99%

Computer Vision-Based Bridge Inspection and Monitoring: A Review

Luo,

Kong,

Zhang

et al. 2023

Sensors

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Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.

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“…In the fourth step, the n-dimensional vector transforms arbitrary data or images into a numerical feature representing an object (Toews and Arbel, 2003). The feature extraction and transformation, such as FFT (Kanwal et al, 2019), Wavelet transform (Tong et al, 2004) ,Gabor filter (Hoang, 2020), Dispersive Phase Stretch Transform (Asghari and Jalali, 2015) are commonly used to extract useful information. In other cases, geometric-based feature extraction and classifiers are used to find geometric features and effectively classify the Image accordingly.…”

Section: Pedestrian Detection Algorithmmentioning

confidence: 99%

Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System

Kumar

Sharma

Kumar

2023

Int. j. math. eng. manag. sci.

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Pedestrian safety has become a critical consideration in developing society especially road traffic, an intelligent transportation need of the hour is the solution left. India tops the world with 11% of global road accidents. With this data, we have moved in the direction of computer vision applications for efficient and accurate pedestrian detection for intelligent transportation systems (ITS). The important application of this research is robot development, traffic management and control, unmanned vehicle driving (UVD), intelligent monitoring and surveillance system, and automatic pedestrian detection system. Much research has focused on pedestrian detection, but sustainable solution-driven research must still be required to overcome road accidents. We have proposed a wireless sensor network-based pedestrian detection system that classifies the real-time set of pedestrian activity and samples the reciprocally received signal strength (RSS) from the sensor node. We applied a histogram of oriented gradient (HOG) descriptor algorithm K-nearest neighbor, decision tree and linear support vector machine to measure the performance and prediction of the target. Also, these algorithms have performed a comparative analysis under different aspects. The linear support vector machine algorithm was trained with 481 samples. The performance achieves the accuracy of 98.90%and has accomplished superior results with a maximum precision of 0.99, recall of 0.98, and F-score of 0.95 with 2% error rate. The model’s prediction indicates that it can be used in the intelligent transportation system. Finally, the limitation and the challenges discussed to provide an outlook for future research direction to perform effective pedestrian detection.

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Image Processing‐Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

Cited by 18 publications

References 50 publications

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Computer Vision-Based Bridge Inspection and Monitoring: A Review

Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System

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