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image processing techniques for Concrete Crack Detection: A Scientometrics Literature Review

REMOTE SENSING 2023年第9期15卷 2400-2400页

作者： Khan, Md. Al-Masrur Kee, Seong-Hoon Pathan, Al-Sakib Khan Nahid, Abdullah-Al Dong A Univ Dept ICT Integrated Ocean Smart Cities Engn Busan 49315 South Korea United Int Univ UIU Dept Comp Sci & Engn Dhaka 1212 Bangladesh Khulna Univ Elect & Commun Engn Discipline Khulna 9208 Bangladesh

Cracks in concrete surfaces are one of the most prominent causes of the degradation of concrete structures such as bridges, roads, buildings, etc. Hence, it is very crucial to detect cracks at an early stage to inspect the structural health of the concrete structure. To solve the drawbacks of manual inspection, image processing techniques (IPTs), especially those based on Deep Learning (DL) methods, have been investigated for the past few years. Due to the groundbreaking development of this field, researchers have devoted their endeavors to detecting cracks using DL-based IPTs and as a result, the techniques have given answers to many challenging problems. However, to the best of our knowledge, a state-of-the-art systematic review paper is lacking in this field that would present a scientometric analysis as well as a critical survey of the existing works to document the research trends and summarize the prominent IPTs for detecting cracks in concrete structures. Therefore, this article comes forward to spur researchers with a systematic review of the relevant literature, which will present both scientometric and critical analysis of the papers published in this research area. The scientometric data that are brought out from the articles are analyzed and visualized by using VOSviewer and CiteSpace text mining tools in terms of some parameters. Furthermore, this article elucidates research from all over the world by highlighting and critically analyzing the incarnated essence of some of the most influential papers. Moreover, this research raises some common questions as well as extracts answers from the analyzed papers to highlight various features of the utilized methods.

关键词： crack detection concrete structures deep learning image processing techniques scientometric analysis

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Enhancing Diabetic Retinopathy Detection with image processing techniques and Deep Learning EfficientNetB0: A Comparative Analysis 20th

Enhancing Diabetic Retinopathy Detection with Image Processi...

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20th International Workshop on Combinatorial image Analysis (IWCIA)

作者： Babu, Sanjana Kumar, Gaurav Haider, Imran SRM Valliammai Engn Coll Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9783031665936;9783031665943

Diabetic retinopathy (DR) is a major global cause of blindness, underscoring the critical need for early and accurate detection. This study addresses this challenge by conducting an in-depth comparative analysis of various image processing techniques coupled with the state-of-the-art EfficientNetB0 model for DR identification. The researchers employed retinal images from the Asia-Pacific Teleophthalmology Society (APTOS) 2019 Blindness Detection competition on Kaggle, a valuable dataset for this purpose. The study considers five image processing methods: Median Subtraction, Ben Graham's Filter, Adaptive Histogram Equalization, Histogram Normalization, and Selective Gamma Correction. These techniques aim to enhance image quality and information content, which is crucial for training the model effectively. The study's primary performance metric is accuracy, recall, precision, and F1 score, which measures the model's ability to classify images correctly. The outcomes reveal that the choice of image preprocessing techniques significantly influences the model's performance. In summary, the study demonstrates that by combining advanced image pre-processing methods with a powerful deep learning model, Ben Graham's Filter can notably enhance diabetic retinopathy detection performance with 86% accuracy. Such advancements are instrumental in the early diagnosis and intervention of DR, thereby mitigating the risk of blindness in affected individuals.

关键词： Diabetic Retinopathy image processing techniques Deep Learning EfficientNetB0 Model Ben Graham Filter

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Advanced Multi-Spectral image processing techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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2024 IEEE International Conference on Recent Advances in Science and Engineering Technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Crack length estimations for small-scale fracture experiments via image processing techniques

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JOURNAL OF MATERIALS RESEARCH 2022年第17期37卷 2848-2861页

作者： Schmuck, K. Alfreider, M. Kiener, D. Univ Leoben Dept Mat Sci Chair Mat Phys Jahnstr 12 Leoben Austria

Accurate knowledge of the current crack length is crucial to evaluate fracture mechanical tests. At the sub-micron to micron scale, the crack length is directly accessible via observation during in-situ experiments in electron microscopes, or indirectly via calculation from sample stiffness. In the current work, image processing techniques were used to introduce a semi-automatic technique to measure crack lengths at the micron scale from image sequences. The technique utilizes manually defined filters and searches for contours near the previous crack tip locating the new one according to the previous position. To demonstrate validity and capability, three micron-sized notched cantilevers were prepared for bending experiments and tested in-situ by partial unloading. Comparison of crack lengths determined by the proposed method, manual measurement and sample stiffness revealed a reasonable agreement, while occasional deviations allow further insights into the crack behaviour. Thus, our new approach enables more in-depth investigation of small-scale fracture processes.

关键词： In situ microcantilever testing Crack propagation image processing techniques Nanocomposite WCu

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image processing techniques represent innovative tools for comparative analysis of proteins

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年第0期117卷 103584-000页

作者： Ahsan, Reza Ebrahimi, Mansour Univ Qom Sch Engn Dept Informat Technol Qom Iran Univ Qom Sch Basic Sci Dept Biol Qom Iran Univ Adelaide Sch Agr & Vet Sci Adelaide SA Australia

Different bioinformatic and data-mining approaches have been used for the analysis of proteins. Here, we describe a novel, robust, and reliable approach for comparative analysis of a large number of proteins by combining image processing techniques and Convolutional Deep Neural Network (IPT-CNN). As proof of principle, we used IPT-CNN to predict different subtypes of Influenza A virus (IAV). Over 8000 sequences of surface proteins haemagglutinin (HA) and neuraminidase (NA) from different IAV subtypes were used to create polynomial or binary vector datasets. The datasets were then converted into binary images. Analysis of these images enabled the classification of IAV subtypes with 100% accuracy and, compared to non-image-based approaches, within a shorter time frame. The proteome-based IPT-CNN approach described here may be used for analysis and proteome-based classification of other proteins.

关键词： image processing techniques Convolutional deep neural network Protein Micro-organism

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The State-of-the-Art Review on Applications of Intrusive Sensing,image processing techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

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Engineering 2021年第6期7卷 845-856页

作者： Yue Hou Qiuhan Li Chen Zhang Guoyang Lu Zhoujing Ye Yihan Chen Linbing Wang Dandan Cao Beijing Key Laboratory of Traffic Engineering Beijing University of TechnologyBeijing 100124China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China National Center for Materials Service Safety University of Science and Technology BeijingBeijing 100083China School of Transportation Southeast UniversityNanjing 211189China Department of Civil and Environmental Engineering Virginia TechBlacksburgVA 24061USA

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and *** service quality and service life are of great importance for civil engineers as they directly affect the regular service for the ***,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation *** pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface *** technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning *** review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

关键词： Pavement monitoring and analysis The state-of-the-art review Intrusive sensing image processing techniques Machine learning methods

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Evaluation of the Adhesion between Aggregate and Asphalt Binder Based on image processing techniques Considering Aggregate Characteristics

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MATERIALS 2023年第14期16卷 5097-5097页

作者： Li, Min Wang, Jian Guo, Zibao Chen, Jingchun Zhao, Zedong Ren, Jiaolong Shandong Univ Technol Sch Civil Engn & Geomat Zibo 255000 Peoples R China CCCC First Highway Northwest Engn Co Ltd Xian 710110 Peoples R China Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China

Aggregate-asphalt adhesion plays an important role in the water stability of asphalt concrete. In various test standards of different countries, it is evaluated via the subjective judgment of testers using the boiling water test. The subjective judgment in the test method is detrimental to the accuracy of the adhesion evaluation. However, there is no quantitative evaluation method for the aggregate-asphalt adhesion in existing studies. Moreover, the effects of aggregate shape on adhesion are also not discussed and stipulated. Hence, an innovative method based on the Chinese boiling water test and image processing technique is put forward to quantificationally evaluate the aggregate-asphalt adhesion. Moreover, the effects of aggregate shapes on adhesion are also investigated via the proposed method from a view of aspect ratio and homogeneity. Results show that the peeling of the asphalt membrane on the aggregate surface is more serious as the complexity of the aggregate shape increases after the boiling water tests, while the effect degree gradually decreases. The effect of aspect ratio on the peeling status of asphalt membrane is lower than that of aggregate homogeneity.

关键词： aggregate-asphalt adhesion evaluation method image processing techniques aggregate characteristics water boiling test

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Tracing 2D Growth of Pancreatic Tumoroids Using the Combination of image processing techniques and Mini-Opto Tomography Imaging System

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TECHNOLOGY IN CANCER RESEARCH & TREATMENT 2023年 22卷

作者： Akbaba, Cihat Ediz Polat, Adem Gokturk, Dilek Adana Alparslan Turkes Sci & Technol Univ Dept Elect & Elect Engn Adana Turkiye Canakkale Onsekiz Mart Univ Dept Elect & Elect Engn TR-17100 Canakkale Turkiye Adana Alparslan Turkes Sci & Technol Univ Dept Bioengn Adana Turkiye

Objectives: In this study, we aimed to trace the 2D growth development of tumoroids produced with MIA PaCa-2 pancreatic cancer cells at different time points. Methods We cultured 3 different tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations and calculated the growth rate of the tumoroids with their images acquired at 9 imaging time points by mini-Opto tomography imaging system applying image processing techniques. We used the metrics contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and mean squared error (MSE) to analyze the distinguishability of the tumoroid structure from its surroundings, quantitatively. Additionally, we calculated the increase of the radius, the perimeter, and the area of 3 tumoroids over a time period. Results In the quantitative assessment, the bilateral and Gaussian filters gave the highest CNR values (ie, Gaussian filter: at each of 9 imaging time points in range of 1.715 to 15.142 for image set-1). The median filter gave the highest values in PSNR in the range of 43.108 to 47.904 for image set-2 and gave the lowest values in MSE in the range of 0.604 to 2.599 for image set-3. The areas of tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations were 1.014 mm(2), 1.047 mm(2), and 0.530 mm(2) in the imaging time point-1 and 33.535 mm(2), 4.538 mm(2), and 2.017 mm(2) in the imaging time point-9. The tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations grew up to times of 33.07, 4.33, and 3.80 in area size over this period, respectively. Conclusions The growth rate and the widest borders of the different tumoroids in a time interval could be detected automatically and successfully. This study that combines the image processing techniques with mini-Opto tomography imaging system ensured significant results in observing the tumoroid's growth rate and enlarging border over time, which is very critical to provide an emerging methodology in vitro cancer studies.

关键词： cancer cells image processing techniques image segmentation pancreatic cancer tumoroid tumors MIA PaCa-2 cells mini-Opto tomography

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Impact of covid-19 in lung cancer detection using image processing techniques, artificial intelligence and machine learning approaches

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SMART SCIENCE 2023年第4期11卷 728-743页

作者： Singh, Kavita Chauhan, Usha Varshney, Lokesh Galgotias Univ Dept Elect Elect & Commun Engn Greater Noida Uttar Pradesh India Galgotias Univ Dept Elect Elect & Commun Engn Greater Noida 203201 Uttar Pradesh India

Due to their impaired immune systems, lung cancer (LC) patients are especially sensitive to COVID-19 and are more susceptible to it as well as its related effects. The diagnosis, treatment and aftercare of LC patients are exceedingly difficult and time-consuming throughout an epidemic due to a multitude of factors. In these situations, the care of LC patients using cutting-edge technologies offers the potential to enhance the diagnosis, treatment, and advancements using machine learning (ML) algorithms and artificial intelligence (AI). The researchers might be able to differentiate between lung problems brought on by the corona virus and those brought on by, for example, chemotherapy and radiation, using therapeutic and imaging data as well as ML techniques. AI ensures that the appropriate individuals are enrolled in LC clinical research more effectively and rapidly than in the past, when it was done in a conventional and time-consuming manner. To effectively treat cancer patients and find new, more potent treatments, it is critical to move past traditional research approaches and make use of artificial intelligence and machine learning (AIML). When applied to various patient populations, AI based algorithms can swiftly identify lung cancer CT scans with COVID-19-linked pneumonia and accurately distinguish non-COVID connected pneumonia, which is significant for thoughtful mechanisms of an outbreak that is significant to AI. It is possible to use the present challenges and projected futures in this study to direct the best application of AI and ML in an epidemic.

关键词： Lung cancer detection image processing techniques covid-19 epidemic artificial intelligence machine learning deep learning

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Study on three-dimensional topography modeling of the grinding wheel with image processing techniques

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2020年第0期167卷 105241-000页

作者： Kang, Mingxia Zhang, Lu Tang, Wencheng Southeast Univ Sch Mech Engn Nanjing 210000 Peoples R China

A novel method for three-dimensional characterization and topography modeling of alumina abrasive grains and single crystal corundum grinding wheel trimmed by a diamond roller is proposed in this paper. An alumina abrasive grain is modeled with a random and arbitrary pyramid determined by the relative position of its five vertices. It is revealed that the relative position is related to the apex angle and volume ratio of abrasive particles. Considering the strong randomness of abrasive grain properties, high-precision characterization of the distribution, size and shape of abrasive grains is extracted through image processing techniques such as identification, segmentation, marking, and removal of connected domains. Meanwhile, a new set of parameters is defined to quantify the identified topographical features of abrasive grains on the wheel surface. It is illustrated that the angle of abrasive grains and its area ratio that can be extracted from dressing tests of the grinding wheel play a dominant role in the establishment of the grinding wheel model. Finally, three-dimensional topography model of the grinding wheel is built with the distribution, size and shape models of active abrasive grains and broken abrasive grains. The results show that the simulated topography model agrees well with the experimental measurements regardless of the distribution, area ratio, size, density or number of abrasive grains.

关键词： Abrasive grain shape Abrasive grain size Abrasive grain distribution image processing techniques Wheel topography

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