TriView-YOLO model enhances cavity detection in challenging soil conditions

Researchers have developed TriView-YOLO, a novel deep learning model designed for detecting subsurface cavities in challenging soft, high-water-content soils. This model utilizes a multi-view fusion approach, integrating three different perspectives of ground-penetrating radar data to improve detection accuracy. Tested primarily on road surveys from Bangkok, Thailand, TriView-YOLO achieved a mean Average Precision (mAP50) of 0.558, demonstrating its effectiveness in conditions where traditional methods struggle. AI

IMPACT This research could improve infrastructure safety by enabling more accurate detection of subsurface cavities in challenging geological conditions.

RANK_REASON The cluster contains a research paper detailing a new model and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Bangkok
• COCO
• DINOv3
• ground-penetrating radar
• Japan
• Sompote Youwai Dr.
• Thailand
• TriView-YOLO
• YOLOv12

• paper
• model release

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