Improved Hard Example Mining Approach for Single Shot Object Detectors

Hard example mining methods generally improve the performance of the object detectors, which suffer from imbalanced training sets. In this work, two existing hard example mining approaches (LRM and focal loss, FL) are adapted and combined in a state-of-the-art real-time object detector, YOLOv5. The effectiveness of the proposed approach for improving the performance on hard examples is extensively evaluated. The proposed method increases mAP by 3% compared to using the original loss function and around 1-2% compared to using the hard-mining methods (LRM or FL) individually on 2021 Anti-UAV Challenge Dataset.

Semi-Automatic Video Annotation For Object Detection

In this study, a semi-automatic video annotation method is proposed which utilizes temporal information to eliminate false-positives with a tracking-by-detection approach by employing multiple hypothesis tracking (MHT). MHT method automatically forms tracklets which are confirmed by human operators to enlarge the training set. A novel incremental learning approach helps to annotate videos in an iterative way. The experiments performed on AUTH Multidrone Dataset reveals that the annotation workload can be reduced up to 96% by the proposed approach.

Effect of Annotation Errors on Drone Detection with YOLOv3

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.