论文标题
注释误差对Yolov3无人机检测的影响
Effect of Annotation Errors on Drone Detection with YOLOv3
论文作者
论文摘要
在深网的最新进展之后，对象检测和跟踪具有深度学习骨架的算法得到了显着改善。但是，这种快速的发展导致了大量带注释的标签的必要性。即使大多数这些数据集的这种半自动注释过程的细节尚不确切地知道，尤其是对于视频注释，通常会使用一些自动标记过程。不幸的是，这种方法可能会导致错误的注释。在这项工作中，对象检测问题的不同类型的注释错误是模拟的，并且在训练和测试阶段检查了流行的最先进的对象检测器Yolov3的性能。此外，还以这种方式检查了CVPR-2020反UAV挑战数据集中的一些不可避免的注释错误，同时提出了一种解决方案来纠正此宝贵数据集的此类注释错误。
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.
