オープンボキャブラリーモデルは建設現場のMEP要素検出に適しているか
Are Open-Vocabulary Models Ready for Detection of MEP Elements on Construction Sites
地上ロボットに搭載したカメラで建設現場のMEP設備を検出する際、オープンボキャブラリー視覚言語モデルと微調整済み軽量閉集合検出器の性能を比較評価した。
著者: Abdalwhab Abdalwhab, Ali Imran, Sina Heydarian, Ivanka Iordanova, David St-Onge
分類: cs.CV, cs.RO
原文アブストラクト
The construction industry has long explored robotics and computer vision, yet their deployment on construction sites remains very limited. These technologies have the potential to revolutionize traditional workflows by enhancing accuracy, efficiency, and safety in construction management. Ground robots equipped with advanced vision systems could automate tasks such as monitoring mechanical, electrical, and plumbing (MEP) systems. The present research evaluates the applicability of open-vocabulary vision-language models compared to fine-tuned, lightweight, closed-set object detectors for detecting MEP components using a mobile ground robotic platform. A dataset collected with cameras mounted on a ground robot was manually annotated and analyzed to compare model performance. The results demonstrate that, despite the versatility of vision-language models, fine-tuned lightweight models still largely outperform them in specialized environments and for domain-specific tasks.
