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.

翻译：硬体样板采矿方法一般会改善物体探测器的性能,因为这种探测器有不均衡的培训组合,在这项工作中,两种现有的硬体样采矿方法(LRM和焦点损失,FL)经过调整,并合并成最先进的实时物体探测器YOLOv5. 广泛评价了改进硬体样板性能的拟议方法的有效性,与最初损失功能相比,拟议方法使MAP增加3%,与2021年反UAV挑战数据集相比,使MAP增加约1-2%,单独使用硬体采矿方法(LRM或FL)。