Effect of Annotation Errors on Drone Detection with YOLOv3 [article]

Aybora Koksal, Kutalmis Gokalp Ince, A. Aydin Alatan
2020 arXiv   pre-print
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
more » ... t 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.
arXiv:2004.01059v3 fatcat:wyetbg7kijfplnjmtqqljhxz3u