mlsanityData to 7 Oct 2026

Improved Hard Example Mining Approach for Single Shot Object Detectors

arXiv 2202.13080 ↗First posted 26 Feb 2022cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 26 Feb 2022.

Builds on

17 of 23 references.

References · 17

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  4. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  5. 2015Fast R-CNNcited by 2,930
  6. 2018YOLOv3: An Incremental Improvementcited by 2,652
  7. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  8. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  9. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  10. 2017Focal Loss for Dense Object Detectioncited by 749
  11. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 719
  12. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 577
5 more
  1. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  2. 2017A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detectioncited by 43
  3. 2018Unsupervised Hard Example Mining from Videos for Improved Object Detectioncited by 30
  4. 2020Effect of Annotation Errors on Drone Detection with YOLOv3cited by 7
  5. 2018Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectorscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2022Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challengecited by 3
  2. 2026TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detectioncited by 2
  3. 2023Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributionscited by 0