Daphnia magna (D. magna) is a model organism widely used in aquatic ecotoxicology research due to its sensitivity to environmental changes. The survival and reproduction rates of D. magna are easily affected by toxic environments. However, their small size, fragility, and transparency, especially in neonate stages, make them challenging to count accurately. Traditionally, counting adult and neonate D. magna relies on manual separation and visual observation, which is not only tedious but also prone to inaccuracies. Previous attempts to aid counting with optical sensors have faced issues such as inducing stress damage due to vertical movement and an inability to distinguish between adults and neonates. With the advancement of deep learning technologies, our study employs a simple light source culture device and utilizes the Mask2Former model to analyze D. magna against the background. Additionally, the U-Net model is used for comparative analysis. We also applied OpenCV technology for automatic counting of adult and neonate D. magna. The model's results were compared against manual counting performed by experienced technicians. Our approach achieves an average relative accuracy of 99.72 % for adult D. magna and 98.30 % for neonate. This method not only enhances counting accuracy but also provides a fast and reliable technique for studying the survival and reproduction rates of D. magna as a model organism.

中文翻译：

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使用机器视觉对大型溞进行自动计数和分类


大溞 （D. magna） 是一种模式生物，由于其对环境变化的敏感性，被广泛用于水生生态毒理学研究。D. magna 的存活率和繁殖率容易受到有毒环境的影响。然而，它们的体积小、易碎和透明，尤其是在新生儿阶段，使它们难以准确计数。传统上，计数成人和新生儿 D. magna 依赖于人工分离和目视观察，这不仅乏味而且容易不准确。以前使用光学传感器帮助计数的尝试遇到了一些问题，例如由于垂直运动而引起应力损伤，以及无法区分成人和新生儿。随着深度学习技术的进步，我们的研究采用了一种简单的光源培养装置，并利用 Mask2Former 模型在背景下分析了 D. magna。此外，U-Net 模型用于比较分析。我们还应用 OpenCV 技术对成人和新生儿 D. magna 进行自动计数。将模型的结果与经验丰富的技术人员进行的手动计数进行了比较。我们的方法对成年 D. magna 的平均相对准确率为 99.72%，对新生儿的平均相对准确率为 98.30%。这种方法不仅提高了计数准确性，而且为研究作为模式生物的 D. magna 的存活率和繁殖率提供了一种快速可靠的技术。