水中ロボットによる建設環境モニタリングのためのロバスト画像処理技術
Robust Image Processing Techniques for Construction Environment Monitoring Using Underwater Robots
実海域の劣化を模擬した合成データで既存のJoint-IDネットワークを再学習し、後処理を加えることで水中画像の画質を改善する手法を提案した。
詳しい要約
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著者: Seunghee Yun, Geonmo Yang, Juhui Lee, Changbeom Park, Jeahyung Choi, Younggun Cho
分類: cs.CV, cs.RO
原文アブストラクト
This paper proposes a robust image processing framework for underwater robot-based construction environment monitoring, targeting complex degradations observed in real marine environments. Unlike conventional approaches that mainly consider absorption and backscattering, real underwater imagery is strongly affected by depth-dependent forward scattering blur and particle-induced degradations such as marine snow. To address this, we introduce a staged processing pipeline that sequentially models background degradation via depth-aware forward scattering and foreground degradation using realistic marine snow patterns extracted from real images. The resulting synthetic data are used to retrain an existing Joint-ID network without modifying its architecture, enabling an isolated evaluation of dataset realism. In addition, a lightweight post-processing scheme is applied to enhance contrast and structural clarity. Experiments on real underwater datasets collected in Korean coastal environments demonstrate consistent improvements in visual quality and UIQM scores. The results indicate that explicitly modeling forward scattering and realistic particle effects effectively reduces the synthetic-to-real gap and improves practical applicability in real-world underwater robotic operations.
