Road Damage Detection Using Yolov9-Based Imagery
Road damage is one of the leading factors contributing to traffic accidents. Rapid identification and repair of damaged roads are crucial in road infrastructure management. This study aims to develop an effective method for detecting road damage, utilizing the YOLOv9 algorithm as a key component, su...
Saved in:
Main Authors: | Febrian Akbar Azhari, Tatang Rohana, Kiki Ahmad Baihaqi, Ahmad Fauzi |
---|---|
Format: | Article |
Language: | English |
Published: |
LPPM ISB Atma Luhur
2025-05-01
|
Series: | Jurnal Sisfokom |
Subjects: | |
Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2377 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Hybrid Model for Early Melanoma Detection: Integrating YOLOv9 and Faster R-CNN for Enhanced Diagnostic Accuracy
by: Mohamed I. Marie, et al.
Published: (2025-01-01) -
YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images
by: Fahrizal, et al.
Published: (2025-06-01) -
YOLO-WTB: Improved YOLOv12n Model for Detecting Small Damage of Wind Turbine Blades From Aerial Imagery
by: Phat T. Nguyen, et al.
Published: (2025-01-01) -
Integration of YOLOv9 Segmentation and Monocular Depth Estimation in Thermal Imaging for Prediction of Estrus in Sows Based on Pixel Intensity Analysis
by: Iyad Almadani, et al.
Published: (2025-06-01) -
Deep Learning-Based Detection and Assessment of Road Damage Caused by Disaster with Satellite Imagery
by: Jungeun Cha, et al.
Published: (2025-07-01)