Zobrazeno 1 - 10
of 576
pro vyhledávání: '"Pavement crack"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Pavement cracks affect the structural stability and safety of roads, making accurate identification of crack for assessing the extent of damage and evaluating road health. However, traditional convolutional neural networks often struggle wit
Externí odkaz:
https://doaj.org/article/e85e674ba1af41ce8a53371c242ae9c3
Autor:
Matarneh, Sandra, Elghaish, Faris, Al-Ghraibah, Amani, Abdellatef, Essam, Edwards, David John
Publikováno v:
Smart and Sustainable Built Environment, 2023, Vol. 14, Issue 1, pp. 1-22.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/SASBE-01-2023-0004
Publikováno v:
Port Said Engineering Research Journal, Vol 28, Iss 2, Pp 18-30 (2024)
Developing an effective system for detecting and classifying pavement cracks is crucial for ensuring traffic safety. However, the procedure of manual inspection for identifying these cracks can be hazardous and time-consuming. Thus, it's essential to
Externí odkaz:
https://doaj.org/article/ee6f987fc3e94fc7a669c4b4152f85ec
Autor:
Saúl Cano-Ortiz, Eugenio Sainz-Ortiz, Lara Lloret Iglesias, Pablo Martínez Ruiz del Árbol, Daniel Castro-Fresno
Publikováno v:
Results in Engineering, Vol 23, Iss , Pp 102745- (2024)
Computer-aided deep learning has significantly advanced road crack segmentation. However, supervised models face challenges due to limited annotated images. There is also a lack of emphasis on deriving pavement condition indices from predicted masks.
Externí odkaz:
https://doaj.org/article/fe368c2e8bb0435abb56ecd66d3b367e
Publikováno v:
In Expert Systems With Applications 10 March 2025 264
Publikováno v:
IEEE Access, Vol 12, Pp 122452-122461 (2024)
Aiming to solve the problem of pavement crack detection in complex road environments, an improved algorithm based on YOLOv5s is proposed. First, the CBAM (Convolutional Block Attention Module) is introduced after the backbone network’s C3 modules t
Externí odkaz:
https://doaj.org/article/43f195cbe8364bd9b099e09a01475792
Autor:
Mianqing He, Tze Liang Lau
Publikováno v:
IEEE Access, Vol 12, Pp 12655-12666 (2024)
The maintenance of pavements takes considerable time and poses a significant task, especially when it comes to detecting cracks at the pixel level. Due to the complexity of pavement conditions, such as road markings, shadows, and oil stains, deep lea
Externí odkaz:
https://doaj.org/article/f0eea32b3c2446d08d463df78520c09d
Publikováno v:
Sensors, Vol 24, Iss 14, p 4751 (2024)
Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, m
Externí odkaz:
https://doaj.org/article/7f2b9c4072e94f3db3af4495675ac458
Publikováno v:
Heliyon, Vol 10, Iss 4, Pp e26142- (2024)
The pavement is vulnerable to damage from natural disasters, accidents and other human factors, resulting in the formation of cracks. Periodic pavement monitoring can facilitate prompt detection and repair the pavement diseases, thereby minimizing ca
Externí odkaz:
https://doaj.org/article/d356159084314f5d80b0119babab8cfc
Publikováno v:
Applied Sciences, Vol 14, Iss 7, p 2909 (2024)
With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic mai
Externí odkaz:
https://doaj.org/article/8d31cc10247e486b834605b4cfa9c5b2