Background

This work presents an experience done to evaluate the number of very small objects in the field of view of a stereo microscope, which are usually counted by direct observation, with or without the use of grids as visual aids. We intend to show that deep learning recent algorithms like YOLO v5 are adequate to use in the evaluation of the number of objects presented, which can easily reach the 1000 s. This kind of algorithm is open-source software, requiring a minimum of skills to install and run on a regular laptop. We further intend to show that the robustness of these kinds of approaches using convolutional neural networks allowed for the use of images of less quality, such as the images acquired with a cell phone.

Results

The results of training the algorithm and counting microalgae in cell phone images were assessed through human curation in a set of test images and showed a high correlation, showing good precision and accuracy in detections.

Conclusions

This is a low-cost alternative available worldwide to many more facilities than expensive cameras and high-maintenance rigid set-ups, along with software packages with a slow learning curve, therefore enlarging the scope of this technique to areas of knowledge where the conditions of laboratory and human work are a limiting factor.

中文翻译：

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使用深度学习在线资源用立体显微镜和手机计数微藻培养物

背景

这项工作介绍了评估体视显微镜视野中非常小物体数量的经验，这些物体通常通过直接观察来计算，无论是否使用网格作为视觉辅助工具。我们打算证明深度学习的最新算法（如 YOLO v5）足以用于评估所呈现的对象数量，可以轻松达到 1000 秒。这种算法是开源软件，需要最低限度的技能才能在普通笔记本电脑上安装和运行。我们进一步打算表明，这些使用卷积神经网络的方法的鲁棒性允许使用质量较低的图像，例如用手机获取的图像。

结果

在一组测试图像中通过人工管理评估了手机图像中算法训练和微藻计数的结果，并显示出高度相关性，显示出良好的检测精度和准确性。

结论

与昂贵的相机和高维护的刚性设置以及学习曲线缓慢的软件包相比，这是一种在全球范围内可供更多设施使用的低成本替代方案，因此将这种技术的范围扩大到知识领域实验室和人工工作是一个限制因素。