ANALISIS KOMPARATIF MODEL YOLO DAN PENGARUH OVERSAMPLING ON-THE-FLY TERHADAP KINERJA DETEKSI KERUSAKAN JALAN: COMPARATIVE ANALYSIS OF YOLO MODELS AND THE INFLUENCE OF ON-THE-FLY OVERSAMPLING ON ROAD DAMAGE DETECTION PERFORMANCE - Sherina Nur Anggraeni; Muljono (LPPM Universitas Abdurrab, 2026)
Automated road damage detection using computer vision technology remains an active research challenge, particularly do to issues of imbalanced datasets and variations in road surface conditions. While deep learning (YOLO) methods have been widely applied, a comprehensive performance comparison of the latest architectures and the impact of data balancing strategies require further analysis. This study aims to compare the performance of the latest YOLO generations (YOLOv8, YOLOv9, YOLOv10, and YOLO11) and conduct an ablation study to determine the influence of on-the-fly oversampling. The research began by collecting road damage image data (pothole, crack, and manhole) from the public source: