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<p>Aquaculture is vital in ensuring a sustainable protein supply for economic purposes. Timely identification of damage to net pens poses a notable challenge in the aquaculture environment. Detecting damaged net pens in underwater environments using remotely operated vehicles (ROVs) offers a safe and efficient solution, eliminating the need for human divers to face potential dangers. Underwater inspections with optical cameras face challenges due to reduced visibility caused by floating particles and light attenuation. So, selecting the best available robot view is vital for clear images for net inspection. To address this issue, we track the mean gradient feature across partial or entire images and actively control the robot’s pose to get the best available images. To simplify setting the desired set-point for distance control, we train a convolutional neural network (CNN) offline using supervised learning and integrate it with a Proportional Integral Derivative (PID) controller. This combined approach enables the ROV to maintain a consistent relative pose with respect to the fishnet, thereby obtaining clear net images in dynamic marine environments and effectively identifying any damage. Experimental results obtained in both the garden pool and the real fish farm environments validate the efficacy of the proposed method.</p>

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Active vision-based real-time aquaculture net pens inspection using ROV

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月24日

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  • Waseem Akram,
  • Muhayy Ud Din,
  • Mohamed Heshmat,
  • Alessandro Casavola,
  • Lakmal Seneviratne,
  • Irfan Hussain

摘要

Aquaculture is vital in ensuring a sustainable protein supply for economic purposes. Timely identification of damage to net pens poses a notable challenge in the aquaculture environment. Detecting damaged net pens in underwater environments using remotely operated vehicles (ROVs) offers a safe and efficient solution, eliminating the need for human divers to face potential dangers. Underwater inspections with optical cameras face challenges due to reduced visibility caused by floating particles and light attenuation. So, selecting the best available robot view is vital for clear images for net inspection. To address this issue, we track the mean gradient feature across partial or entire images and actively control the robot’s pose to get the best available images. To simplify setting the desired set-point for distance control, we train a convolutional neural network (CNN) offline using supervised learning and integrate it with a Proportional Integral Derivative (PID) controller. This combined approach enables the ROV to maintain a consistent relative pose with respect to the fishnet, thereby obtaining clear net images in dynamic marine environments and effectively identifying any damage. Experimental results obtained in both the garden pool and the real fish farm environments validate the efficacy of the proposed method.