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We collected the following usage statistics from biigle.de:
Laser point detection was attempted on 159368 images. Of these detections, 103059 (65 %) were successful. Of the successful cases, 23423 (23 %) were automatic detections. This means that only 15 % of the attempted laser point detections were successful automatic detections.
If we assume that users prefer automatic detections and only annotate manually if the detection fails, we could conclude that the laser point detection algorithm (as implemented here) does not work very well. There could be a need for a better and carefully tested algorithm that can replace DELPHI.
The text was updated successfully, but these errors were encountered:
It's probably possible to train a deep learning laser point detection model based on the data we have gathered in BIIGLE. This could be investigated in a student project.
We collected the following usage statistics from biigle.de:
Laser point detection was attempted on 159368 images. Of these detections, 103059 (65 %) were successful. Of the successful cases, 23423 (23 %) were automatic detections. This means that only 15 % of the attempted laser point detections were successful automatic detections.
If we assume that users prefer automatic detections and only annotate manually if the detection fails, we could conclude that the laser point detection algorithm (as implemented here) does not work very well. There could be a need for a better and carefully tested algorithm that can replace DELPHI.
The text was updated successfully, but these errors were encountered: