YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images
Roads are crucial infrastructure for connectivity and transportation. Various asphalt pavement distresses pose safety risks to road users and vehicles. Early detection of these distresses is crucial for road safety and efficiency. This study proposes employing the deep learning algorithm YOLOv9, the...
Saved in:
| Main Authors: | Fahrizal, Siti Nurjanah, Yoan Purbolingga, Dila Marta Putri, Asde Rahmawati, Bastul Wajhi Akramunnas, Muhidin Arifin |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Riau
2025-06-01
|
| Series: | International Journal of Electrical, Energy and Power System Engineering |
| Subjects: | |
| Online Access: | https://ijeepse.id/journal/index.php/ijeepse/article/view/239 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Geogrid Reinforcement of Asphalt Pavements
by: Adam Zofka, et al.
Published: (2017-09-01) -
Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms
by: Hong Zhang, et al.
Published: (2025-03-01) -
Investigation of indoor and field tests on asphalt pavement with inverted asphalt layers based on the vertical vibration compaction method
by: Yong Yi, et al.
Published: (2024-12-01) -
Determination of Equivalent Temperature for Asphalt Pavement Design in Vietnam
by: Quang Phuc NGUYEN, et al.
Published: (2024-12-01) -
Investigation of Asphalt Concrete Pavement Quality of Lithuanian Highways
by: Evaldas Petkevicius, et al.
Published: (2006-06-01)