This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

Abstract
1. 서 론
2. 관련이론
    2.1 딥러닝 물체 검출
3. 딥러닝 기반 물체 검출 및 거리 추출 모델
    3.1 딥러닝 물체 검출 모델 YOLOv2
    3.2 제안 하는 모델 및 비용 함수
    3.3 모델 학습
4. 실험결과
    4.1 실험 환경
    4.2 학습 데이터
    4.3 차량 검출 및 거리 추출
5. 결 론
References

• 김현우(Mobility Platform Research Center, Korea Electronics Technology Institute) | Hyunwoo Kim
• 박상현(Mobility Platform Research Center, Korea Electronics Technology Institute) | Sanghyun Park