As drones gain more popularity these days, drone detection becomes more important part of the drone systems for safety, privacy, crime prevention and etc. However, existing drone detection systems are expensive and heavy so that they are only suitable for industrial or military purpose. This paper proposes a novel approach for training Convolutional Neural Networks to detect drones from images that can be used in embedded systems. Unlike previous works that consider the class probability of the image areas where the class object exists, the proposed approach takes account of all areas in the image for robust classification and object detection. Moreover, a novel loss function is proposed for the CNN to learn more effectively from limited amount of training data. The experimental results with various drone images show that the proposed approach performs efficiently in real drone detection scenarios.

1. 서 론
 2. YOLOv2의 특징과 한계점
 3. 심층망 구조 및 학습
  3.1 심층망 구조
  3.2 심층망의 클래스 확률 및 confidence 학습
  3.3 앵커 박스의 중심 좌표 및 크기 학습
  3.4 최적화 학습을 위한 loss의 설정
 4. 실험 및 결과
 5. 결 론
 References

• 이동현(Electrical Engineering, Kumoh National Institute of Technology) | Dong-Hyun Lee Corresponding author