Zobrazeno 1 - 10
of 7 362
pro vyhledávání: '"crack detection"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract The accurate detection of tunnel lining cracks and prompt identification of their primary causes are critical for maintaining tunnel availability. The advancement of deep learning, particularly in the domain of convolutional neural network (
Externí odkaz:
https://doaj.org/article/9ea7e7af78a34d5ea2ec148b544ddf70
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 3815-3826 (2024)
Abstract Unsupervised anomaly detection, often approached as a one‐class classification problem, is a critical task in computer vision. Knowledge distillation has emerged as a promising technique for enhancing anomaly detection accuracy, especially
Externí odkaz:
https://doaj.org/article/e9f4007f62f94808bc0f1db74e64a0c5
Autor:
Chong WANG, Yuhui ZHU
Publikováno v:
Zhongguo Jianchuan Yanjiu, Vol 19, Iss 5, Pp 95-106 (2024)
ObjectiveAs traditional ship crack detection methods based on artificial visual inspection and ultrasonic methods in ship repair and inspection processes have the characteristics of low efficiency, high cost and high danger, a ship crack detection me
Externí odkaz:
https://doaj.org/article/efc8f3b778784564bc6bdfca9146cf56
Autor:
Devi Willieam Anggara, Mohd Shafry Mohd Rahim, Riyadh Zulkifli, Abdul Rashid Husain, Riyanto, Mazleenda Mazni, Izni Syahrizal Ibrahim, Suhono Harso Supangkat
Publikováno v:
Applications of Modelling and Simulation, Vol 8, Pp 272-282 (2024)
Concrete structure damage and severity are determined by examining the width of cracks in various forms. This categorisation of cracks is based on their specific types, which is crucial for engineers and professionals. It allows them to efficiently p
Externí odkaz:
https://doaj.org/article/3a2b8ea9f0304ee486bdf188fb8c7e17
Publikováno v:
Journal of Infrastructure Preservation and Resilience, Vol 5, Iss 1, Pp 1-19 (2024)
Abstract Accurate crack detection is crucial for maintaining pavement integrity, yet manual inspections remain labor-intensive and prone to errors, underscoring the need for automated solutions. This study proposes a novel crack segmentation approach
Externí odkaz:
https://doaj.org/article/d694a3ffd4e547e3bdc245787d6e1d55
Publikováno v:
Alexandria Engineering Journal, Vol 104, Iss , Pp 745-752 (2024)
Underwater concrete structure crack detection and structural health condition assessment based on image processing is a challenging task. The complex underwater environment and severe image degradation seriously affect the accuracy of crack detection
Externí odkaz:
https://doaj.org/article/f7895fc2d27346639c218f130334e771
Publikováno v:
HighTech and Innovation Journal, Vol 5, Iss 3, Pp 690-702 (2024)
This study explores a non-destructive testing (NDT) method for crack detection using a two-stage convolutional neural network (CNN) model, incorporating a combination of AlexNet and YOLO models through transfer learning. Crack detection is pivotal fo
Externí odkaz:
https://doaj.org/article/0379ed24267346849413834402e03620
Publikováno v:
Discover Civil Engineering, Vol 1, Iss 1, Pp 1-16 (2024)
Abstract In the inspection and diagnosis of concrete construction, crack detection is highly recommended in the earliest phases to prevent any potential risks later. However, the flaws in concrete surfaces cannot be reliably and effectively identifie
Externí odkaz:
https://doaj.org/article/d888e306e2604995a7d5e4776fba6ab0
Autor:
Turan Arslan, Emirhan Mustafa Anık
Publikováno v:
Uludağ University Journal of The Faculty of Engineering, Vol 29, Iss 2, Pp 555-566 (2024)
Karayolu esnek üstyapılarındaki çatlaklar genellikle trafik yükleri ve hava koşullarından kaynaklanır. Bu çatlakların genişlemeden tespit edilip gerekli bakımlarının yapılması, yol konforunun sürekliliğini sağlamanın yanı sıra b
Externí odkaz:
https://doaj.org/article/f59c688de91144f6921e17935ac474a0
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-27 (2024)
Abstract U-net, a fully convolutional network-based image segmentation method, has demonstrated widespread adaptability in the crack segmentation task. The combination of the semantically dissimilar features of the encoder (shallow layers) and the de
Externí odkaz:
https://doaj.org/article/56d8bc6d7b0247919b432f9942fb39b8