创新性水产养殖生物计量分析:利用红外激光器和 ToF 摄像机跟踪微型鱼类幼体

Alisa Kunapinun, William Fairman, Paul S. Wills, S. Mejri, Magaleate Kostelnik, Bing Ouyang
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摘要

在水产养殖场运行和研究范围内,监测鱼类幼体可提供有关养殖场运行状况的关键数据。例如,缺氧可能导致异常运动。目前,对这些微小实体(1 毫米大小)的精确监测取决于卓越的水体透明度和专业设备。虽然绿激光可能是远距离水下成像的首选,但它对鱼是可见的。因此,它会干扰鱼类,并可能损害它们的视觉系统。这一点在我们位于港湾分部海洋研究所(HBOI)的设施中尤为突出。为了应对这些挑战,我们的研究将最初配备 50 毫米镜头的飞行时间(ToF)照相机改装成了使用红外激光的显微成像仪。这种装置能够进行详细但窄景深的成像,适用于清水条件。最近的改进包括过渡到 25 毫米镜头,增强了相机在中等浊度条件下捕捉更宽图像(鱼卵宽约 20 像素)和观察更精细细节的能力,但景深减少了 5 毫米。这种改装使照相机的用途转向观察非常小的生物体(100-200 微米),并降低了其在高度浑浊水域进行深度测量的效果。这种调整确保了对鱼类幼虫的更精确跟踪,并且由于使用了红外光,成像过程对鱼眼无害。机器学习技术的集成进一步完善了该系统在不同水域条件下准确识别鱼类幼虫的能力。我们的方法提供了一个平衡的解决方案,既经济实惠,又提高了准确性,还考虑到了鱼类的福利,为鱼类幼虫跟踪领域做出了积极贡献。
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Innovative aquaculture biometrics analysis: harnessing IR lasers and ToF cameras for microscopic fish larvae tracking
Within the scope of aquaculture farm operation and research, monitoring fish larvae offers pivotal data about the operational conditions of the farm. For example, hypoxia may induce abnormal movements. Currently, precise monitoring of these diminutive entities (1 mm in size) hinges on superior water clarity and specialized equipment. While green laser may be preferred for extended range underwater imaging, it is visible to the fish. Hence it will disturb fish and potentially damage their vision system. This is of particular concern at our facility at the Harbor Branch Oceanographic Institute (HBOI). To address these challenges, our research has adapted a Time-of-Flight (ToF) camera, equipped initially with a 50mm lens, into a microscopic imager using an IR laser. This setup was capable of detailed but narrow depth field imaging, suitable for clear water conditions. Recent advancements have included transitioning to a 25mm lens, enhancing the camera’s ability to capture wider images (approximately 20 pixels wide for fish eggs) and observe finer details in medium turbidity conditions, though with a reduced depth field of 5mm. This modification has shifted the camera’s utility towards observing very small living organisms (100-200 microns) and reduced its effectiveness in depth measurement in highly turbid waters. This adaptation ensures more precise tracking of fish larvae and offers a fish-eye-safe imaging process due to the use of IR light. The integration of machine learning techniques further refines the system’s ability to accurately identify fish larvae in varying water conditions. Our approach presents a balanced solution, combining affordability, improved accuracy, and mindful consideration of the fish’s welfare, contributing positively to the field of fish larvae tracking.
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