Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 142, С. 109887 - 109887
Опубликована: Дек. 28, 2024
Язык: Английский
Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 142, С. 109887 - 109887
Опубликована: Дек. 28, 2024
Язык: Английский
Alexandria Engineering Journal, Год журнала: 2024, Номер 106, С. 298 - 311
Опубликована: Июль 13, 2024
Язык: Английский
Процитировано
11Information Fusion, Год журнала: 2025, Номер unknown, С. 103036 - 103036
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
1Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
7Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Март 12, 2025
Urban infrastructure, particularly in ageing cities, faces significant challenges maintaining building aesthetics and structural integrity. Traditional methods for detecting diseases on exteriors, such as manual inspections, are often inefficient, costly, prone to errors, leading incomplete assessments delayed maintenance actions. This study explores the application of advanced deep learning techniques accurately detect exterior surfaces buildings urban environments, aiming enhance detection efficiency accuracy while providing a real-time monitoring solution that can be widely implemented infrastructure health management. The research model improves feature extraction by integrating DenseNet blocks Swin-Transformer prediction heads, trained validated using dataset 289 high-resolution images collected from diverse environments China. Data augmentation improved model's robustness against varying conditions. proposed achieved high rate 84.42%, recall 77.83%, an F1 score 0.81, with speed 55 frames per second. These metrics demonstrate effectiveness identifying complex damage patterns, minute cracks, even within noisy significantly outperforming traditional methods. highlights potential transform strategies offering practical ultimately enhancing contributing practices timely interventions.
Язык: Английский
Процитировано
0Alexandria Engineering Journal, Год журнала: 2025, Номер 122, С. 453 - 464
Опубликована: Март 18, 2025
Язык: Английский
Процитировано
0Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 156, С. 111090 - 111090
Опубликована: Май 24, 2025
Язык: Английский
Процитировано
0Electronics, Год журнала: 2024, Номер 13(16), С. 3214 - 3214
Опубликована: Авг. 14, 2024
In addressing the critical issue of right-of-way conflicts in mixed-traffic environments, this paper introduces a novel shared driving strategy that encompasses two guiding frameworks for resolution. The first framework applies to active lane changing. Before changing occurs, allocates right way autonomous vehicles (AVs). Based on allocated way, AVs decide whether send request relevant vehicles. To enhance lane-changing comfort, vehicle assesses variance roll and lateral acceleration exceeds preset threshold, ultimately deciding proceed with change. second pertains passive After detecting an obstacle, way. calculate based their speed distance from using information determine change lanes or decelerate order avoid obstacle. If is chosen, further evaluation necessary. improve compare pitch longitudinal acceleration, then they proposed has been validated various scenarios, including high-speed (105 km/h), low (13 general scenarios obstacles at 125 m. results show effectively functions both low-speed scenarios.
Язык: Английский
Процитировано
1Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 138, С. 109462 - 109462
Опубликована: Окт. 22, 2024
Процитировано
0Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 142, С. 109887 - 109887
Опубликована: Дек. 28, 2024
Язык: Английский
Процитировано
0