![]() ![]() To secure a wide variety of images, it is necessary to collect data at the actual site. In particular, training data that include various types of crack images have a better effect on crack detection performance than those including images consisting of similar shapes. This also applies to crack detection algorithms. Generally, greater numbers of training data can improve the performance of deep-learning algorithm. ![]() For example, recognition performance decreases when trained deep neural network models are applied to new sites. The proposed inspection technology for concrete structures is expected to be implemented in the future in connection with various automation techniques.ĭespite such rapid advancements, considerable room for improvement of crack detection technologies remains. Experimental results show that the proposed algorithm achieved a crack detection performance with a mean intersection-over-union of 84.53% and an F1 score of 82.91%. Furthermore, a method to reconstruct the 3-dimensional shape of cracks is proposed using a stereo-vision-based triangulation measurement technique that determines the size of detected cracks. ![]() To resolve this issue, we propose a new deep neural network that applies an optimal mixing ratio of training data to improve recognition performance alongside an adversarial learning-based balanced ensemble discriminator network. Unfortunately, the performance of existing crack detection technologies decreases under environmental conditions that vary widely. In recent years, deep-learning-based computer vision technologies have emerged as a promising trend and have been actively used for crack detection. Hence, the detection of cracks in concrete is a key component of structural management. Thus, periodic diagnoses and inspections are essential because such conditions can eventually lead to disaster. The functional performance of concrete structures degrades over time as a result of continuous loads, stress fatigue, and external environmental changes. All subjects Allied Health Cardiology & Cardiovascular Medicine Dentistry Emergency Medicine & Critical Care Endocrinology & Metabolism Environmental Science General Medicine Geriatrics Infectious Diseases Medico-legal Neurology Nursing Nutrition Obstetrics & Gynecology Oncology Orthopaedics & Sports Medicine Otolaryngology Palliative Medicine & Chronic Care Pediatrics Pharmacology & Toxicology Psychiatry & Psychology Public Health Pulmonary & Respiratory Medicine Radiology Research Methods & Evaluation Rheumatology Surgery Tropical Medicine Veterinary Medicine Cell Biology Clinical Biochemistry Environmental Science Life Sciences Neuroscience Pharmacology & Toxicology Biomedical Engineering Engineering & Computing Environmental Engineering Materials Science Anthropology & Archaeology Communication & Media Studies Criminology & Criminal Justice Cultural Studies Economics & Development Education Environmental Studies Ethnic Studies Family Studies Gender Studies Geography Gerontology & Aging Group Studies History Information Science Interpersonal Violence Language & Linguistics Law Management & Organization Studies Marketing & Hospitality Music Peace Studies & Conflict Resolution Philosophy Politics & International Relations Psychoanalysis Psychology & Counseling Public Administration Regional Studies Religion Research Methods & Evaluation Science & Society Studies Social Work & Social Policy Sociology Special Education Urban Studies & Planning BROWSE JOURNALS ![]()
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