Effect of Annotation Errors on Drone Detection with YOLOv3
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by
Aybora Koksal, Kutalmis Gokalp Ince, A. Aydin Alatan
2021
Abstract
Following the recent advances in deep networks, object detection and tracking
algorithms with deep learning backbones have been improved significantly;
however, this rapid development resulted in the necessity of large amounts of
annotated labels. Even if the details of such semi-automatic annotation
processes for most of these datasets are not known precisely, especially for
the video annotations, some automated labeling processes are usually employed.
Unfortunately, such approaches might result with erroneous annotations. In this
work, different types of annotation errors for object detection problem are
simulated and the performance of a popular state-of-the-art object detector,
YOLOv3, with erroneous annotations during training and testing stages is
examined. Moreover, some inevitable annotation errors in CVPR-2020 Anti-UAV
Challenge dataset is also examined in this manner, while proposing a solution
to correct such annotation errors of this valuable data set.
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