Section for Fisheries Technology, National Institute of Aquatic Resources, Technical University of Denmark, Hirtshals, Denmark.
Section for Oceans and Arctic, National Institute of Aquatic Resources, Technical University of Denmark, Lyngby, Denmark.
PLoS One. 2021 Jun 16;16(6):e0252824. doi: 10.1371/journal.pone.0252824. eCollection 2021.
Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability can be challenged by low light conditions, mobilized sediment and scattering in murky waters. In this study, we introduce a novel observation system (called NepCon) which aims at reducing current limitations by combining an optimized image acquisition setup and tailored image analyses software. The NepCon system includes a high-contrast background to enhance the visibility of the target objects, a compact camera and an artificial light source. The image analysis software includes a machine learning algorithm which is evaluated here to test automatic detection and count of Norway lobster (Nephrops norvegicus). NepCon is specifically designed for applications in demersal trawls and this first phase aims at increasing the accuracy of N. norvegicus detection at the data acquisition level. To find the best contrasting background for the purpose we compared the output of four image segmentation methods applied to static images of N. norvegicus fixed in front of four test background colors. The background color with the best performance was then used to evaluate computer vision and deep learning approaches for automatic detection, tracking and counting of N. norvegicus in the videos. In this initial phase we tested the system in an experimental setting to understand the feasibility of the system for future implementation in real demersal fishing conditions. The N. norvegicus directed trawl fishery typically has no assistance from underwater observation technology and therefore are largely conducted blindly. The demonstrated perception system achieves 76% accuracy (F-score) in automatic detection and count of N. norvegicus, which provides a significant elevation of the current benchmark.
水下视频监测系统在渔业中被广泛用于调查鱼类在渔具中的行为以及渔具在捕鱼过程中的性能。这种系统也可以用于评估渔获物的组成。然而，在底层拖网渔业中，由于光线条件差、沉积物移动和浑浊水域的散射，其适用性可能受到挑战。在本研究中，我们引入了一种新的观测系统（称为 NepCon），旨在通过优化图像采集设置和定制的图像分析软件来克服当前的限制。NepCon 系统包括一个高对比度的背景，以增强目标物体的可见度，一个紧凑的摄像头和一个人工光源。图像分析软件包括一个机器学习算法，我们在这里对其进行评估，以测试挪威龙虾（Nephrops norvegicus）的自动检测和计数。NepCon 专门设计用于底层拖网应用，这第一阶段旨在提高在数据采集层面上对挪威龙虾的检测精度。为了找到最适合的对比背景，我们比较了四种图像分割方法在固定在四个测试背景颜色前面的挪威龙虾静态图像上的输出。然后，使用性能最佳的背景颜色来评估计算机视觉和深度学习方法在视频中自动检测、跟踪和计数挪威龙虾的效果。在这个初始阶段，我们在实验环境中测试了该系统，以了解该系统在未来实际底层捕捞条件下实施的可行性。挪威龙虾定向拖网渔业通常没有水下观测技术的辅助，因此在很大程度上是盲目进行的。所展示的感知系统在自动检测和计数挪威龙虾方面的准确率达到 76%（F 分数），这显著提高了当前的基准。
