Automatic non-biting midge (Chironomidae) identification through the application of object detection and deep learning techniques
Masters Thesis
Hollister, J. 2020. Automatic non-biting midge (Chironomidae) identification through the application of object detection and deep learning techniques. Masters Thesis Canterbury Christ Church University School of Psychology and Life Sciences
Authors | Hollister, J. |
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Type | Masters Thesis |
Qualification name | Masters by Research |
Abstract | This research study introduces a possible new method for the identification of chironomid larvae mounted on microscope slides in the form of an automatic computer-based |
Keywords | Automatic non-biting midge; Identification; Object detection ; Deep learning techniques |
Year | 2020 |
File | File Access Level Open |
Supplemental file | File Access Level Restricted |
Publication process dates | |
Deposited | 13 Sep 2021 |
https://repository.canterbury.ac.uk/item/8yq80/automatic-non-biting-midge-chironomidae-identification-through-the-application-of-object-detection-and-deep-learning-techniques
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