自律飛行ドローンと深層学習による被災建物屋内の損傷・生存者検出
Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles
自律航法と深層学習を組み合わせ、低コストな小型ドローンで被災建物屋内の構造損傷と生存者を自動検出する手法を提案し、模擬被災オフィスでの実験で高精度を確認した。
著者: Xiao Pan, Sina Tavasoli, T. Y. Yang, Sina Poorghasem
分類: cs.CV, cs.RO
原文アブストラクト
Rapid response to natural disasters such as earthquakes is a crucial element in ensuring the safety of civil infrastructures and minimizing casualties. Traditional manual inspection is labour-intensive, time-consuming, and can be dangerous for inspectors and rescue workers. This paper proposed an autonomous inspection approach for structural damage inspection and survivor detection in the post-disaster building indoor scenario, which incorporates an autonomous navigation method, deep learning-based damage and survivor detection method, and a customized low-cost micro aerial vehicle (MAV) with onboard sensors. Experimental studies in a pseudo-post-disaster office building have shown the proposed methodology can achieve high accuracy in structural damage inspection and survivor detection. Overall, the proposed inspection approach shows great potential to improve the efficiency of existing manual post-disaster building inspection.