深海ゴミをAIで見つける：水中ゴミ検出アルゴリズムの評価
Robotic Detection of Marine Litter Using Deep Visual Detection Models
自律水中ロボットによる海洋ゴミ回収を目指し、実海域のゴミ画像データセットで複数の深層学習物体検出モデルを訓練・評価し、処理能力の異なる3機種でリアルタイム性も検証した。
著者: Michael Fulton, Jungseok Hong, Md Jahidul Islam, Junaed Sattar
分類: cs.RO
原文アブストラクト
Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. This paper evaluates a number of deep-learning algorithms preforming the task of visually detecting trash in realistic underwater environments, with the eventual goal of exploration, mapping, and extraction of such debris by using AUVs. A large and publicly-available dataset of actual debris in open-water locations is annotated for training a number of convolutional neural network architectures for object detection. The trained networks are then evaluated on a set of images from other portions of that dataset, providing insight into approaches for developing the detection capabilities of an AUV for underwater trash removal. In addition, the evaluation is performed on three different platforms of varying processing power, which serves to assess these algorithms' fitness for real-time applications.
