Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Analysis of the Effect of Deep-learning Super-resolution for Fragments Detection Performance Enhancement

파편 탐지 성능 향상을 위한 딥러닝 초해상도화 효과 분석

  • Yuseok Lee (Defense System Test Center, Agency for Defense Development)
  • 이유석 (국방과학연구소 국방시험연구원)
  • Received : 2023.02.12
  • Accepted : 2023.05.30
  • Published : 2023.06.05
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Abstract

The Arena Fragmentation Test(AFT) is designed to analyze warhead performance by measuring fragmentation data. In order to evaluate the results of the AFT, a set of AFT images are captured by high-speed cameras. To detect objects in the AFT image set, ResNet-50 based Faster R-CNN is used as a detection model. However, because of the low resolution of the AFT image set, a detection model has shown low performance. To enhance the performance of the detection model, Super-resolution(SR) methods are used to increase the AFT image set resolution. To this end, The Bicubic method and three SR models: ZSSR, EDSR, and SwinIR are used. The use of SR images results in an increase in the performance of the detection model. While the increase in the number of pixels representing a fragment flame in the AFT images improves the Recall performance of the detection model, the number of pixels representing noise also increases, leading to a slight decreases in Precision performance. Consequently, the F1 score is increased by up to 9 %, demonstrating the effectiveness of SR in enhancing the performance of the detection model.

Keywords

Acknowledgement

이 논문은 2023년 정부(방위사업청)의 재원으로 국방과학연구소에서 수행된 연구임(딥러닝 기술을 적용한 영상처리 기반 탄두 파편데이터 계측 및 정밀분석 기술).

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