Diatom detection has been a challenging task for computer scientist and biologist during past years. In this work, the new state of art techniques based on the deep learning framework have been tested, in order to check whether they are suitable for this purpose. On the one hand, RCNNs (Region based Convolutional Neural Networks), which select candidate regions and applies a convolutional neural network and, on the other hand, YOLO (You Only Look Once), which applies a single neural network over the whole image, have been tested. The first one is able to reach poor results in out experimentation, with an average of 0.68 recall and some tricky aspects, as for example it is needed to apply a bounding box merging algorithm to get stable detections; but the second one gets remarkable results, with an average of 0.84 recall in the evaluation that have been carried out, and less aspects to take into account after the detection has been performed. Future work related to parameter tuning and processing are needed to increase the performance of deep learning in the detection task. However, as for classification it has been probed to provide succesfully performance.
Anibal Pedraza, Gloria Bueno, Oscar Deniz, Jesus Ruiz-Santaquiteria, Carlos Sanchez, Saul Blanco, Maria Borrego-Ramos, Adriana Olenici, and Gabriel Cristobal, "Lights and pitfalls of convolutional neural networks for diatom identification," Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 106790G (Presented at SPIE Photonics Europe: April 24, 2018; Published: 24 May 2018); https://doi.org/10.1117/12.2309488.
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