Deep learning–based drone detection with SWIR cameras
0
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
Small unmanned aerial vehicles, commonly known as drones, and their related industries are improving in leaps and bounds. The global drone industry began with a military focus and subsequently progressed into commercial applications. Consequently, abuse cases linked to drone technology are gradually increasing. Following the technical advancement in drone technology, studies on drone detection and prevention are actively ongoing. This is one such study. Radar-based drone detection that combines various existing sensors or equipment has shortcomings, including high costs and specialist operations. Thus, this paper proposes a drone-detection system that uses only thermal images from short-wavelength infrared (SWIR) cameras. The YOLO model, which is widely used for object recognition, was used for the drone-detection algorithm. Labels were attached to 22,921 thermal images to test the constructed system; 16,121 images were used for training and the remainder for testing. The test results showed 98.17% precision and 98.65% recall. Learning through drone-image shooting in various environments, after removing static from clouds and other noise, is expected to improve detection performance in the future.
Abstract
Small unmanned aerial vehicles, commonly known as drones, and their related industries are improving in leaps and bounds. The global drone industry began with a military focus and subsequently progressed into commercial applications. Consequently, abuse cases linked to drone technology are gradually increasing. Following the technical advancement in drone technology, studies on drone detection and prevention are actively ongoing. This is one such study. Radar-based drone detection that combines various existing sensors or equipment has shortcomings, including high costs and specialist operations. Thus, this paper proposes a drone-detection system that uses only thermal images from short-wavelength infrared (SWIR) cameras. The YOLO model, which is widely used for object recognition, was used for the drone-detection algorithm. Labels were attached to 22,921 thermal images to test the constructed system; 16,121 images were used for training and the remainder for testing. The test results showed 98.17% precision and 98.65% recall. Learning through drone-image shooting in various environments, after removing static from clouds and other noise, is expected to improve detection performance in the future.
참고문헌 (16)
- H. -G. Kim, "Analysis on patent trends for industry of unmanned aerial vehicle", 2017 Fall Conference of the Korean Entertainment Industry Association, pp.90~93 (2017)
- "Commercialization Promotion Agency for R&D Outcomes", S&T Market Report, 67, pp.10~ (2019)
- S. H. Choi, "Recent R&D trends of anti-drone technologies", Electronics and Telecommunications Trends, 33(3), pp.78~88 (2018)
- 이동현, "심층 컨벌루션 신경망 기반의 실시간 드론 탐지 알고리즘", 로봇학회 논문지, 12(4), pp.425~431 (2017), 한국로봇학회[DOI]
- C. Seol, "X-band phased array antenna radar design for drone detection", 2019 Summer Conference of the Institute of Electronics and Information Engineers, pp.363~365 (2019)
- B. T. Koo, "Implementation of AESA based intelligent radar system for small drone detection", 2019 Fall Conference of the Institute of Electronics and Information Engineers, pp.933~934 (2019)
- 이기웅, "ISAR 영상 기반 소형 드론 탐지 구현", 한국전자파학회 논문지, 28(2), pp.159~162 (2017), 한국전자파학회[DOI]
- J. Redmon, "You only look once : Unified, real-time object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.779~788 (2016)
- C. Szegedy, "Going deeper with convolutions", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1~9 (2015)
- A. Bochkovskiy, "YOLOv4: Optical speed and accuracy of object detection", arXiv preprint arXiv:2004.10934, ~ (2020)
- J. Redmon, "YOLOv3: An incremental improvement", arXiv preprint arXiv:1804.02767, ~ (2018)
- C. -Y. Wang, "CSPNet: A new backbone that can enhance learning capability of CNN", Proceedings of the IEEE, 2020, 390~391
- K. He, "Spatial pyramid pooling in deep convolutional networks for visual recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), pp.1904~1916 (2015)
- S. Liu, "Path aggregation network for instance segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.8759~8768 (2018)
- J. -K. Kim, "Faraway small drone detection based on deep learning", International Journal of Computer Science and Network Security, 20(1), pp.149~154 (2020)
- "GitHub - darkpgmr/DarkLabel: Video/Image Labeling and Annotation Tool"[URL]