|
|
|
|
|
| Villon, Sébastien; Mouillot, David; Chaumont, Marc; Subsol, Gérard; Claverie, Thomas; Villéger, Sébastien. |
| Processing data from surveys using photos or videos remains a major bottleneck in ecology. Deep Learning Algorithms (DLAs) have been increasingly used to automatically identify organisms on images. However, despite recent advances, it remains difficult to control the error rate of such methods. Here, we proposed a new framework to control the error rate of DLAs. More precisely, for each species, a confidence threshold was automatically computed using a training dataset independent from the one used to train the DLAs. These species-specific thresholds were then used to post-process the outputs of the DLAs, assigning classification scores to each class for a given image including a new class called “unsure”. We applied this framework to a study case... |
| Tipo: Text |
|
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00640/75244/75406.pdf |
| |
|
|
|
