Effect of Annotation Errors on Drone Detection with YOLOv3
This addresses the problem of annotation errors in object detection datasets for researchers and practitioners, but it is incremental as it builds on existing methods.
The paper investigates how simulated annotation errors affect the performance of YOLOv3 in drone detection, finding that errors degrade accuracy, and proposes a correction method for the CVPR-2020 Anti-UAV Challenge dataset.
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.