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    Título
    Lights and pitfalls of convolutional neural networks for diatom identification
    Autor
    Pedraza, Aníbal
    Bueno, Gloria
    Déniz, Óscar
    Ruiz-Santaquiteria Alegre, Jesús
    Sánchez Bueno, Carlos
    Blanco Lanza, Saúl
    Borrego Ramos, María
    Olenici, Adriana
    Cristóbal Pérez, Gabriel
    Facultad/Centro
    Facultad de Ciencias Biologicas y Ambientales
    Área de conocimiento
    Ecologia
    Datos de la obra
    Anibal Pedraza, Gloria Bueno, Oscar Déniz, Jesus Ruiz-Santaquiteria,Carlos Sanchez, Saul Blanco, Maria Borrego-Ramos, Adriana Olenici,Gabriel Cristobal, "Lights and pitfalls of convolutional neural networks fordiatom identification," Proc. SPIE 10679, Optics, Photonics, and DigitalTechnologies for Imaging Applications V, 106790G (24 May 2018): doi:10.1117/12.2309488
    Editor
    SPIE
    Fecha
    2018-05-24
    Abstract
    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.
    Materia
    Ecología. Medio ambiente
    Palabras clave
    Deep learning
    CNN
    RCNN
    YOLO
    Diatoms detection
    Water quality
    Peer review
    SI
    URI
    http://hdl.handle.net/10612/9312
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