Traditional Visual Inspection Research Articles

A Self-Training-Based System for Die Defect Classification

With increasing wafer sizes and diversifying die patterns, automated optical inspection (AOI) is progressively replacing traditional visual inspection (VI) for wafer defect detection. Yet, the defect classification efficacy of current AOI systems in our case company is not optimal. This limitation is due to the algorithms’ reliance on expertly designed features, reducing adaptability across various product models. Additionally, the limited time available for operators to annotate defect samples restricts learning potential. Our study introduces a novel hybrid self-training algorithm, leveraging semi-supervised learning that integrates pseudo-labeling, noisy student, curriculum labeling, and the Taguchi method. This approach enables classifiers to autonomously integrate information from unlabeled data, bypassing the need for feature extraction, even with scarcely labeled data. Our experiments on a small-scale set show that with 25% and 50% labeled data, the method achieves over 92% accuracy. Remarkably, with only 10% labeled data, our hybrid method surpasses the supervised DenseNet classifier by over 20%, achieving more than 82% accuracy. On a large-scale set, the hybrid method consistently outperforms other approaches, achieving up to 88.75%, 86.31%, and 83.61% accuracy with 50%, 25%, and 10% labeled data. Further experiments confirm our method’s consistent superiority, highlighting its potential for high classification accuracy in limited-data scenarios.

Modal Identification Techniques for Concrete Dams: A Comprehensive Review and Application

Throughout history, the implementation of structural health monitoring systems has played a crucial role in evaluating the responses of dams to environmental and human-induced threats. By continuously monitoring structural integrity and analyzing dynamic characteristics, these systems offer a robust alternative to traditional visual inspection methods, ensuring the long-term safety of dams. This paper delves into the intricate process of operational modal analysis applied to dams, encompassing data collection, preprocessing, and the utilization of diverse modal identification techniques across both time and frequency domains. Moreover, it explores innovative approaches aimed at overcoming challenges encountered in previous methodologies. Also, the evolution of automated modal identification techniques and their application in dams are investigated. It explores the advancements in this field and their implications for enhancing the efficiency and accuracy of modal analysis processes. Furthermore, this paper evaluates the effectiveness of damage detection methods in dams based on operational modal identification.

An image Analysis Algorithm for Detecting Modified Content on the Internet Against Cybercrime. A Technique for Estimating the Probability of Modification

Aim: The aim of this study is to present a cutting-edge image analysis algorithm designed to estimate the probability of modifications in digital images, a critical component in cybersecurity for detecting altered content on the Internet. This technique enhances the accuracy of change detection by distinguishing real inconsistencies from accidental changes. As the problem of sophisticated image processing software and fake images continues to rise on the Internet, the issue of image authenticity becomes increasingly crucial in many areas of science and society. Traditional visual inspection methods are inadequate, as research shows human perception is limited in recognizing the authenticity of images. This study aims to improve the accuracy of detecting modifications by distinguishing authentic inconsistencies from random anomalies Project and methods: This project involves developing an algorithm that leverages noise analysis and a statistical validation step to detect image modifications. The algorithm employs the False Positive Rate Index (FPRI) to manage false positives, providing a reliable confidence level for each detection. The method includes visual exploration to interpret detections and compares the algorithm’s performance with other state-of-the-art techniques in scenarios such as retouching, coloring, and merging. The algorithm analyzes noise in images, considering changes in its statistical properties due to image processing like noise reduction, demosaicing, chromatic aberration correction, color matching, gamma correction, and image compression. It compares global noise curves with local ones to identify potential changes, with results visualized using heat maps. Results: The algorithm demonstrates promising performance across various test scenarios, successfully identifying true modifications among random events. However, it shows limitations in detecting certain types of forgeries, such as internal copy transfer and merging in high-noise areas. Tests conducted on various data sets, including scenarios of retouching, coloring, and merging, confirm the algorithm’s effectiveness in detecting modifications. The results are compared with other noise analysis methods such as Splicebuster, Noiseprint, and Mahdian, showing superior performance in most cases. Conclusions: The proposed method represents a significant advance in image forgery detection within the field of cybersecurity, offering rigorous statistical validation and a measurable level of detection confidence. Despite some challenges in detecting specific types of manipulation, the algorithm is a valuable tool in digital image analysis and forensic research, enhancing the reliability of image alteration detection. As the use of digital technologies increases, so does the importance of image interpretation in areas like fire investigations and other incidents involving records from both classical and thermographic imaging systems. This method is particularly valuable in forensic investigations, where objective evidence of image authenticity is crucial. Future challenges include the use of neural networks to replicate the characteristic noise of original images to hide alterations, underscoring the need for continued development of advanced forgery detection techniques. Keywords: wykrywanie modyfikacji obrazu, cyberbezpieczeństwo, analiza obrazu, wykrywanie fałszywych obrazów

Dual‐Locked Fluorescent Probes Activated by Aminopeptidase N and the Tumor Redox Environment for High‐Precision Imaging of Tumor Boundaries

AbstractClear delineation of tumor margins is essential for accurate resection and decreased recurrence rate in the clinic. Fluorescence imaging is emerging as a promising alternative to traditional visual inspection by surgeons for intraoperative imaging. However, traditional probes lack accuracy in tumor diagnosis, making it difficult to depict tumor boundaries accurately. Herein, we proposed an offensive and defensive integration (ODI) strategy based on the “attack systems (invasive peptidase) and defense systems (reductive microenvironment)” of multi‐dimensional tumor characteristics to design activatable fluorescent probes for imaging tumor boundaries precisely. Screened out from a series of ODI strategy‐based probes, ANQ performed better than traditional probes based on tumor unilateral correlation by distinguishing between tumor cells and normal cells and minimizing false‐positive signals from living metabolic organs. To further improve the signal‐to‐background ratio in vivo, derivatized FANQ, was prepared and successfully applied to distinguish orthotopic hepatocellular carcinoma tissues from adjacent tissues in mice models and clinical samples. This work highlights an innovative strategy to develop activatable probes for rapid diagnosis of tumors and high‐precision imaging of tumor boundaries, providing more efficient tools for future clinical applications in intraoperative assisted resection.

Dual-Locked Fluorescent Probes Activated by Aminopeptidase N and the Tumor Redox Environment for High-Precision Imaging of Tumor Boundaries.

Clear delineation of tumor margins is essential for accurate resection and decreased recurrence rate in the clinic. Fluorescence imaging is emerging as a promising alternative to traditional visual inspection by surgeons for intraoperative imaging. However, traditional probes lack accuracy in tumor diagnosis, making it difficult to depict tumor boundaries accurately. Herein, we proposed an offensive and defensive integration (ODI) strategy based on the "attack systems (invasive peptidase) and defense systems (reductive microenvironment)" of multi-dimensional tumor characteristics to design activatable fluorescent probes for imaging tumor boundaries precisely. Screened out from a series of ODI strategy-based probes, ANQ performed better than traditional probes based on tumor unilateral correlation by distinguishing between tumor cells and normal cells and minimizing false-positive signals from living metabolic organs. To further improve the signal-to-background ratio in vivo, derivatized FANQ, was prepared and successfully applied to distinguish orthotopic hepatocellular carcinoma tissues from adjacent tissues in mice models and clinical samples. This work highlights an innovative strategy to develop activatable probes for rapid diagnosis of tumors and high-precision imaging of tumor boundaries, providing more efficient tools for future clinical applications in intraoperative assisted resection.

ARTIFICIAL INTELLIGENCE BASED SUSTAINABLE COTTON CROP PRODUCTION USING REMOTE SENSING DATASET

The decline in cotton production in Pakistan has directly impacted the country's foreign exchange earnings, plummeting from twelve million bales in 1991, the number decreased to four million bales in 2020. Considering a substantial portion of Pakistan's exports are linked to the textiles area, with 40% of jobs tied to production, this decline is concerning. Factors such as climate change and changes in biotic and abiotic stresses have contributed to substantial losses in cotton production. In Pakistan, where 90% of farmers face resource constraints, there is a pressing need for more accurate mechanisms to understand the challenges in crop production. Timely acknowledgment and adoption of more flexible approaches to irrigation, fertilizer application, and pest and disease control are crucial. Excessive use of pesticides has exacerbated the situation, leading to the development of resistance among pests and diseases. Traditional visual inspection methods alone are insufficient for predicting crop health effectively, resulting in deprived crop yields and financial losses for agrarians. Enhancing crop health condition requires leveraging information gathered from remote sensing and terrestrial sensors, along with truth field surveys. By processing this data through tools like the Decision Support System for Agrotechnology Transfer (DSSAT) and employing machine learning techniques such as artificial neural networks, more accurate local and field-level advice can be provided to farmers. This approach aims to optimize crop management practices, reduce losses, and ultimately improve farmers' incomes.

Using Thermography Infrared to Detect Diabetic Foot Ulcers in Asia Pacific Countries: Scoping Review

Introduction: Diabetes Mellitus (DM) is a growing global health concern, particularly in Indonesia, where the prevalence is projected to rise significantly. Diabetic Foot Ulcer (DFU) is a severe complication of DM that escalates the risk of amputations. Early detection of DFUs plays a crucial role in preventing associated complications Objective: This review aimed to assess the efficacy of infrared thermography in detecting DFUs in the Asia-Pacific region. Methods: A scoping review methodology adhering to PRISMA guidelines was employed. Databases were searched from 2011-2021. PICO strategy used keyword Diabetic foot ulcer, Skin ulcer, Leg ulcer, Foot ulcer, Diabetic neuropathic, Thermography, Temperature mapping, and Detection from PubMed, Scopus, Google Scholar, and Science Direct. Quality of article assessed by JBI. Results: Four (4) studies meeting the inclusion criteria were identified, primarily cross-sectional in design. The results indicated promising sensitivity and specificity of infrared thermography in detecting DFUs among DM patients. The technology's ability to capture thermal images revealing temperature variations associated with tissue inflammation, a common DFU indicator, underscores its efficacy. This non-invasive technique allows for early detection and monitoring of DFUs, facilitating timely interventions and improving patient outcomes. Additionally, infrared thermography provides a quantitative and objective assessment of tissue health, complementing traditional visual inspection and palpation methods. Its effectiveness lies in detecting subtle skin temperature changes, aiding in early identification and management of DFUs before they progress to more severe stages. Conclusion: Infrared thermography shows significant potential in detecting DFUs early, which is vital for effective management and prevention of severe complications. Despite the promising results, broader and more rigorous studies are necessary to validate these findings across different populations and healthcare settings. Further research across diverse settings and study designs is essential to reinforce these findings.

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Surface defects in bearings not only affect the appearance but also impact the service life and performance. Therefore, it is imperative for bearing manufacturers to conduct quality inspections before bearings leave the factory. However, traditional visual inspection methods exhibit shortcomings such as high omission rates, insufficient feature fusion and oversized models when dealing with multiple target defects in bearings. To address these challenges, this paper proposes a surface defect detection method for bearings based on an improved Yolov8 algorithm (G-Yolov8). Firstly, a C3Ghost convolutional module based on the Ghost module is constructed in YOLOv8 to simplify model computational costs. Secondly, a global attention mechanism module is designed at the end of the backbone network to increase sensitivity to implicit small target area features and optimize feature extraction efficiency. Subsequently, a deep deformable convolution feature pyramid network is constructed by introducing the deformable convolutional networks version 2 (DCNv2) and the lightweight content-aware reassembly of features upsampling operator to reduce sampling information loss and improve the fusion of multi-scale target defects. Finally, different attention mechanisms are embedded in the detection network to construct a multi-attention detection head to replace the decoupled head, refining classification and localization tasks, reducing feature confusion, and improving the model’s detection accuracy. Experimental results demonstrate that the improved algorithm achieves a 3.5% increase in mean average precision on a self-made small-scale train bearing surface defect dataset, with a 17.3% reduction in model size. This improvement not only enhances accuracy but also addresses the requirement for lightweight deployment in subsequent stages.

Deep learning techniques to detect rail indications from ultrasonic data for automated rail monitoring and maintenance

The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

Linear discriminant analysis based hidden Markov model for detection of Mysticetes’ vocalisations

Mysticetes’ produce distinctive vocalisations which are used for echolocation, communication, and other marine functions. These cryptic vocalisations are studied by marine scientist to determine the behavioural patterns and movement of this suborder of cetaceans within their ecosystem. In practice, these vocalisations are gathered using passive acoustic monitoring over days, weeks, months, and even years. Therefore, it is complex to study these sounds using traditional visual inspection techniques because the gathered datasets are huge. Machine learning (ML) tools such as Gaussian mixture models (GMMs), support vector machines (SVMs), and hidden Markov models (HMMs) have been adopted in recent times to proffer analytic solutions to automatically detect and study these cryptic vocalisations. Notwithstanding, the feature extraction techniques employed play a vital role in determining the performance of these ML tools. In most cases, the performance of the feature extraction technique is directly proportional to the performance of the ML tools. Thus, the method of linear discriminant analysis (LDA) is introduced in this article as a feature extraction technique that can be adapted with the HMMs (LDA-HMM) to seamlessly detect the vocalisations of Mysticetes. The performance of the proposed LDA-HMM detector is compared with other recent detectors for Mysticetes’ vocalisations in the literature using two different species: Humpback whale songs and Bryde’s whale pulses. Experimental results show that the developed LDA-HMM detector is a performance-efficient alternative in comparison to the recent detection techniques studied in this article. Besides, the LDA-HMM detector offers less computational time complexity; as such, it is more suitable for real-time applications.

Detection of poikilocytosis cells in anemia using (ANN) & embedded systems

In the bloodstream, erythrocytes stand as vital carriers of oxygen and carbon dioxide, facilitated by the presence of hemoglobin within their structures. However, deviations in erythrocyte size can lead to the formation of Poikilocyte cells, a characteristic feature of conditions like Iron Deficiency Anemia. Variants of Poikilocytoses, such as Degmacyte, Dacrocyte, Schistocyte, and Elliptocyte, denote distinct alterations in erythrocyte morphology, often associated with diminished iron levels crucial for haemoglobin synthesis. In a recent study, the differentiation between normal RBCs and Poikilocyte cells has been addressed through the application of Artificial Neural Network (ANN) algorithms, leveraging extracted features from digital images of blood smears. This approach offers a more precise means of identifying blood disorders compared to traditional visual inspection, utilizing image analysis techniques to detect deviations in color, size, and statistical parameters. The methodology involves a series of computational steps including preprocessing, segmentation, morphological operations, feature extraction, and classification, all executed within the Matlab environment. Furthermore, to enhance diagnostic capabilities, the system integrates glucose level measurement alongside erythrocyte analysis, transmitting data to a controller which relays results via GSM signal as SMS and LCD display. This comprehensive approach not only automates cell identification and classification but also ensures efficient and accurate analysis, including the automated separation of overlapped cells.

Patterns of failure for recurrent head and neck squamous cell carcinoma treated with salvage surgery and postoperative IMRT reirradiation

Purpose/ObjectivesThe purpose of this study was to evaluate patterns of locoregional recurrence (LRR) after surgical salvage and adjuvant reirradiation with IMRT for recurrent head and neck squamous cell cancer (HNSCC). Materials/MethodsPatterns of LRR for 61 patients treated consecutively between 2003 and 2014 who received post-operative IMRT reirradiation to ≥ 60 Gy for recurrent HNSCC were determined by 2 methods: 1) physician classification via visual comparison of post-radiotherapy imaging to reirradiation plans; and 2) using deformable image registration (DIR). Those without evaluable CT planning image data were excluded. All recurrences were verified by biopsy or radiological progression. Failures were defined as in-field, marginal, or out-of-field. Logistic regression analyses were performed to identify predictors for LRR. ResultsA total of 55 patients were eligible for analysis and 23 (42 %) had documented LRR after reirradiation. Location of recurrent disease prior to salvage surgery (lymphatic vs. mucosal) was the most significant predictor of LRR after post-operative reirradiation with salvage rate of 67 % for lymphatic vs. 33 % for mucosal sites (p = 0.037). Physician classification of LRR yielded 14 (61 %) in-field failures, 3 (13 %) marginal failures, and 6 (26 %) out-of-field failures, while DIR yielded 10 (44 %) in-field failures, 4 (17 %) marginal failures, and 9 (39 %) out-of-field failures. Most failures (57 %) occurred within the original site of recurrence or first echelon lymphatic drainage. Of patients who had a free flap placed during salvage surgery, 56 % of failures occurred within 1 cm of the surgical flap. ConclusionOur study highlights the role of DIR in enhancing the accuracy and consistency of POF analysis. Compared to traditional visual inspection, DIR reduces interobserver variability and provides more nuanced insights into dose-specific and spatial parameters of locoregional recurrences. Additionally, the study identifies the location of the initial recurrence as a key predictor of subsequent locoregional recurrence after salvage surgery and re-IMRT.

Automatic foot ulcer segmentation using conditional generative adversarial network (AFSegGAN): A wound management system.

Effective wound care is essential to prevent further complications, promote healing, and reduce the risk of infection and other health issues. Chronic wounds, particularly in older adults, patients with disabilities, and those with pressure, venous, or diabetic foot ulcers, cause significant morbidity and mortality. Due to the positive trend in the number of individuals with chronic wounds, particularly among the growing elderly and diabetes populations, it is imperative to develop novel technologies and practices for the best practice clinical management of chronic wounds to minimize the potential health and economic burdens on society. As wound care is managed in hospitals and community care, it is crucial to have quantitative metrics like wound boundary and morphological features. The traditional visual inspection technique is purely subjective and error-prone, and digitization provides an appealing alternative. Various deep-learning models have earned confidence; however, their accuracy primarily relies on the image quality, the dataset size to learn the features, and experts' annotation. This work aims to develop a wound management system that automates wound segmentation using a conditional generative adversarial network (cGAN) and estimate the wound morphological parameters. AFSegGAN was developed and validated on the MICCAI 2021-foot ulcer segmentation dataset. In addition, we use adversarial loss and patch-level comparison at the discriminator network to improve the segmentation performance and balance the GAN network training. Our model outperformed state-of-the-art methods with a Dice score of 93.11% and IoU of 99.07%. The proposed wound management system demonstrates its abilities in wound segmentation and parameter estimation, thereby reducing healthcare workers' efforts to diagnose or manage wounds and facilitating remote healthcare.

Defect identification method for ultrasonic inspection of pipeline welds based on cross-modal zero-shot learning

Ultrasonic inspection of pipeline welds still uses the traditional visual inspection signal method to identify pipeline defects. The identification of defects relies entirely on the subjective judgment of practitioners and is highly dependent on their level of experience. Deep learning models have achieved very good results in classification tasks, but they rely on a large number of annotated data samples for each category. However, it is difficult to collect a large number of samples with different defects and annotate them for the classification of pipe welding defects. Based on the idea of zero-shot learning (ZSL), which makes full use of experts’ semantic descriptions of defect categories, artificial semantic features are integrated cross-modally with ultrasonic inspection signal features. In this way, a common semantic space containing seen and unseen classes is constructed to achieve the detection of various defects. Meanwhile, to alleviate the problem of extreme imbalance of training data between the seen and unseen classes in ZSL model training, a ZSL model Feature-GAN-ZSL (FGZ) fused with a generative adversarial network (GAN) is proposed. The model utilizes a Feature-GAN network to generate unseen class features during training and adds a classifier to enhance the generation of features with stronger discriminative power. In the experiments, sample data for porosity, incomplete penetration, and cracks were used as visible classes, and samples for incomplete fusion and slag entrapment were used as unseen classes. Five state-of-the-art models in the ZSL domain were compared. The results show that the FGZ model has a good ability to recognize various defects, not only the types of defects that participated in the training but also the defects that did not participate in the training. This plays a perfect role in dealing with various pipeline welding defects.

Crack Detection of Concrete Images Using Dilatation and Crack Detection Algorithms

Crack detection in structures is an important and time-consuming element of monitoring the health of structures and ensuring structural safety. The traditional visual inspection of structures can be unsafe and may produce inconsistent results. Thus, there is a need for a method to easily and accurately identify and analyze cracks. In this study, algorithms for automatically detecting the size and location of cracks in concrete images were developed. Cracks were automatically detected in a total of 10 steps. In steps 5 and 9, two user algorithms were added to increase crack detection accuracy, where 1000 crack images and 1000 non-crack images were used, respectively. In the crack image, 100% of the cracks were detected, but 95.3% of the results were very good, even if the results that were not bad in terms of quality were excluded. In addition, the accuracy of detecting non-crack images was also very good (96.9%). Thus, it is expected that the crack detection algorithm presented in this study will be able to detect the location and size of cracks in concrete. Moreover, these algorithms will help in observing the soundness of structures and ensuring their safety.

Multiclass seismic damage detection of buildings using quantum convolutional neural network

AbstractThe traditional visual inspection technique for damage assessment of buildings immediately after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies have been carried out using deep learning techniques, particularly convolutional neural network (CNN), to evaluate the damage to building structures after an earthquake using buildings’ damage images. Quantum computing, on the other hand, is a computing environment that can exploit superposition and entanglement, which are not available in classical computing environments, to achieve higher performance using parallelism between qubits. This paper presents a novel quantum CNN (QCNN) approach to detect damage to reinforced concrete (RC) buildings from images after the earthquake. The QCNN model is developed and trained using the RC building damaged images collected from past earthquakes. The performance of this model is evaluated based on the multiclass damage detection ability of the real‐world RC building damaged images collected from the recent earthquake in Turkey in February 2023. Furthermore, the seismic damage detection accuracy obtained from the QCNN model is compared with various CNN architecture results.

Scratch Detection Method of Shaft Parts Based on Improved YOLOv5

The pre-extracted target object is always distorted due to many parts spots, surface reflection and other factors in traditional visual inspection. Therefore, a detection process based on improved YOLOv5 is proposed. In the early stage, for the optimization of light source, the circular light is generally used to successfully extract the region of interest, so as to reduce the scratches caused by image pretreatment, resulting in detection omission and distortion. Adding the ACON (All Class in One Network, ACON) activation function can improve the network feature extraction ability and the accuracy of the detection algorithm. Bounding box loss, means of target detection loss and classification loss are all significantly improved by the improved algorithm, far exceeding the performance of traditional detection methods, and all have a certain degree of reduction.

Automated Visual Inspection of Defects in Transparent Display Layers Using Light-Field 3-D Imaging

Since a display panel comprises multiple layered components, defects may occur within different layers through manufacturing processes. Traditional visual inspection systems with a 2D camera cannot identify the occurrence location among layers. Several 3D imaging technologies, such as CT, TSOM, and MRI, suffer from slow performance and a large form factor. In this work, we propose a novel visual inspection method to detect defects on a display panel using light-field 3D imaging. Without powering the target display panel, we first acquire the high-resolution depth information of defects located inside the transparent layers. We then convert the depth information to the object coordinate system to estimate the physical locations of defects. We automatically classify the types of defects and their layer locations along the depth axis in multiple transparent layers of the display panel. Lastly, our experimental results validate that our method can successfully detect and classify various display defects.

Detecting vineyard plants stress in situ using deep learning

Diseases and nutritional deficiencies have the potential to seriously impact the production yield and proper development of perennial species such as grapevine. The distinction between changes resulting from normal growth stages and plant alterations caused by biotic and abiotic stress is often drawn through visual inspection, where the observer’s subjectivity could introduce human errors, despite the presence of experience and technical knowledge. This document presents an assessment of CNNs for detecting plant stress in grapevine RGB images captured in situ, under conditions that could include variations in light, shadows, insects, or the presence of scrubs. We evaluated five architectures for their ability to discriminate plants with stress symptoms in images captured through the annual grapevine cycle in field conditions. The best model exhibited a 97.2% accuracy, 0.996 ROC AUC, and 0.958 AP using the EfficientNetB3 architecture. Our methodology aims to support winegrowers in their decision-making by enhancing the information they collect through traditional visual inspection methods.

Applications of Terrestrial Laser Scanner in Detecting Pavement Surface Defects

An entire roadway system represents a crucial element in the sustainable urban transportation planning process. Pavement surfaces are at continual risk of accumulating serious deteriorations and defects throughout their service life due to traffic loading and environmental impact. Since roadway networks are growing rapidly, relying on visual pavement inspection is not always feasible. Therefore, this paper proposes an effective assessment method for evaluating flexible pavement surface distresses using a terrestrial laser scanner (TLS) and calculating the pavement condition index (PCI). The proposed terrestrial laser scanner method results in road condition assessments becoming faster, safer, and more systematic. It also aims to determine the geometric characteristics of the investigated roads. A major road in Egypt was selected to test the proposed technique and compare it with the traditional visual inspection method. The evaluation was carried out to assess different types of pavement distress, such as cracking, rutting, potholes, and raveling distresses. Every pavement distress was defined in terms of surface area, the width of the crack, and intensity, and the data from TLS were then processed by MAGNET COLLAGE software. A MATLAB program was developed to match the TLS observational data to plane equations. PAVER software was also used to determine the PCI values for each TLS position. The revealed distresses for the investigated road using TLS observations reveal a significant improvement in determining flexible pavement distresses and geometric characteristics.