Zhao Junhong, Xue Bing, Vennel Ross, Zhang Mengjie
Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.
Coastal and Freshwater Group, Cawthron Institute, Nelson, New Zealand.
J R Soc N Z. 2024 Apr 25;55(6):1563-1588. doi: 10.1080/03036758.2024.2345316. eCollection 2025.
As the mussel farming industry expands, particularly in regions such as New Zealand, there is a growing need for advanced monitoring and management solutions to ensure sustainability and operational efficiency. The current reliance on manual infrequent observation of aquaculture structures limits farmers' ability to monitor them in real time. Addressing these challenges, large-scale 3D reconstruction provides a practical solution by facilitating the creation of replicable depictions of mussel farm scenes and floatation buoys derived from recorded video, supporting off-site evaluation and enabling precise decision making. This paper introduces a novel approach to enhance the visualisation and monitoring of mussel farm floatation through a hierarchical reconstruction process. In contrast to earlier studies, we focus on recovering not only the overall environment (background) but also the finer details of key elements such as buoys to create a comprehensive representation of mussel farm geometry and appearance. We propose to segment the scene into the background and granular object instances and reconstruct them separately in a multi-stage process. The initial 3D scene reconstruction is performed using the Structure-from-Motion (SfM) technique, leveraging video footage captured by a vessel-mounted camera. This coarse reconstruction serves as the foundation for subsequent fine-grained enhancements. To recover finer details, object tracking is applied and the trajectories obtained are then conjunct with geometry triangulation to determine the real-world positions of individual buoys. A multiple-scale denoising method, grounded in dominant direction correlation, is implemented to eliminate non-reliable tracking objects and reduce reconstruction artifacts, ensuring the accuracy of the final results. This hierarchical reconstruction approach contributes to the advancement of mussel farm management by offering a powerful technology for comprehensive visualisation, enabling farmers to make informed decisions based on a detailed understanding of the mussel farm's geometry and dynamics.
随着贻贝养殖产业的扩张，尤其是在新西兰等地区，对先进的监测和管理解决方案的需求日益增长，以确保可持续性和运营效率。目前对水产养殖结构的人工不定期观测限制了养殖户实时监测它们的能力。为应对这些挑战，大规模三维重建提供了一个切实可行的解决方案，它通过利用录制视频创建贻贝养殖场场景和漂浮浮标的可复制描绘，支持场外评估并实现精确决策。本文介绍了一种通过分层重建过程来增强贻贝养殖场漂浮物可视化和监测的新方法。与早期研究不同，我们不仅专注于恢复整体环境（背景），还关注浮标等关键元素的更精细细节，以创建贻贝养殖场几何形状和外观的全面表示。我们建议将场景分割为背景和颗粒状对象实例，并在多阶段过程中分别对它们进行重建。初始的三维场景重建使用运动结构（SfM）技术，利用安装在船上的摄像头拍摄的视频素材。这种粗略重建为后续的细粒度增强奠定基础。为了恢复更精细的细节，应用对象跟踪，然后将获得的轨迹与几何三角测量相结合，以确定各个浮标的实际位置。实施一种基于主导方向相关性的多尺度去噪方法，以消除不可靠的跟踪对象并减少重建伪影，确保最终结果的准确性。这种分层重建方法通过提供一种用于全面可视化的强大技术，有助于推进贻贝养殖场管理，使养殖户能够基于对贻贝养殖场几何形状和动态的详细了解做出明智决策。
