원문정보
초록
영어
In this paper, for the purpose of designing an real-time unmanned monitoring system, the YOLOv5s (small) object detection model was applied on the NVIDIA TX2TM AI (Artificial Intelligence) edge computing platform in order to design the fundamental function of an unmanned monitoring system that can detect objects in real time. YOLOv5s was applied to the our real-time unmanned monitoring system based on the performance evaluation of object detection algorithms (for example, R-CNN, SSD, RetinaNet, and YOLOv5). In addition, the performance of the four YOLOv5 models (small, medium, large, and xlarge) was compared and evaluated. Furthermore, based on these results, the YOLOv5s model suitable for the design purpose of this paper was ported to the NVIDIA TX2TM AI edge computing system and it was confirmed that it operates normally. The real-time unmanned monitoring system designed as a result of the research can be applied to various application fields such as an security or monitoring system. Future research is to apply NMS (Non-Maximum Suppression) modification, model reconstruction, and parallel processing programming techniques using CUDA (Compute Unified Device Architecture) for the improvement of object detection speed and performance.
목차
1. Introduction
2. Related Works
3. Real-time Unmanned Monitoring System Design
3.1 Requirements of Real-time Unmanned Monitoring System
3.2 Design of Real-time Unmanned Monitoring System
4. Performance Evaluation and Results
4.1 Performance Evaluation Environment of Object Detection [18],[19],[20]
4.2 Object Detection Performance Evaluation
4.3 Designing a Real-Time Unmanned Monitoring System on NVIDIA TX2TM AI computing platform
4.4 Performance Evaluation and Review of Results
5. Results
Acknowledgement
References