| Autor: |
Maria da Conceição Proença, Miguel Barbosa, Ana Amorim |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Bulletin of the National Research Centre, Vol 46, Iss 1, Pp 1-6 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2522-8307 |
| DOI: |
10.1186/s42269-022-00965-z |
| Popis: |
Abstract 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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