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Improving On-board Aircraft Target Detection Model: Utilizing Shape Information and Mixed Precision Quantization

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(12), pp.13-20
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 8, 2024
  • Accepted : November 27, 2024
  • Published : December 31, 2024

Junhyeong Bak 1 Yong-ho Choi 1 Jong-Won Moon 1 Bora Whang 1 Ingu Park 1

1엘아이지넥스원(주)

Accredited

ABSTRACT

The object detection technology in the field of computer vision is utilized to detect aircraft targets using electro-optical systems. Recent advancements in object detection, driven by deep neural networks and hardware accelerators, have led to significant performance improvements, which can also be applied to aircraft target detection. However, for successful integration into weapon systems, it is essential to achieve high detection reliability and real-time processing in on-board environments. This paper presents two strategies to address these challenges. These strategies focus on improving model training by utilizing object shape information and optimizing the model with mixed precision computation. The experimental results demonstrate that the methods improve the performance of the aircraft target detection model and enhance its applicability to weapon systems.

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