We present a low-cost monocular 3D position estimation method for perception in aquaculture monitoring. Video surveillance of aquaculture has many advantages but given the size of farms and the complexity of their habitats, it is not feasible for farmers to continuously monitor fish health. We formulate a novel end-to-end deep visual learning pipeline called Aqua3DNet that estimates fish pose using a bottom-up approach to detect and assign key features in one pass. In addition, a depth estimation model using Saliency Object Detection (SOD) masks is implemented to track the 3D position of the fish over time, which is used in this paper to create 3D density heat maps of the fish. The evaluation of the algorithm's performance shows that the detection accuracy reaches 80.63%, the F1 score reaches 87.34%, and the frames per second (fps) reaches 5.12. Aqua3DNet achieves comparable performance to other aquaculture-based computer vision and depth estimation models, with minimal decrease in speed despite the synthesis of the two models.

我们提出了一种用于水产养殖监测感知的低成本单目 3D 位置估计方法。水产养殖视频监控有很多优点，但考虑到养殖场的规模及其栖息地的复杂性，农民持续监测鱼类健康状况并不可行。我们制定了一种名为 Aqua3DNet 的新颖的端到端深度视觉学习管道，它使用自下而上的方法来估计鱼的姿势，以一次性检测和分配关键特征。此外，还实现了使用显着性对象检测 (SOD) 掩模的深度估计模型来跟踪鱼随时间变化的 3D 位置，本文使用该模型来创建鱼的 3D 密度热图。算法性能评估表明，检测准确率达到80.63%，F1得分达到87.34%，每秒帧数（fps）达到5。