Improved Hard Example Mining Approach for Single Shot Object Detectors

Date

2022-10-18

Author

Köksal, Aybora
Tuzcuoğlu, Önder
Ince, Kutalmiş Gökalp
Ataseven, Yoldaş
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

URI

https://doi.org/10.1109/ICIP46576.2022.9897806
https://hdl.handle.net/11511/100822

DOI

https://doi.org/10.1109/icip46576.2022.9897806

Conference Name

2022 IEEE International Conference on Image Processing (ICIP)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

A. Köksal, Ö. Tuzcuoğlu, K. G. Ince, Y. Ataseven, and A. A. Alatan, “Improved Hard Example Mining Approach for Single Shot Object Detectors,” presented at the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, Fransa, 2022, Accessed: 00, 2022. [Online]. Available: https://doi.org/10.1109/ICIP46576.2022.9897806.