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In this paper, an urban object detection system via unmanned aerial vehicles (UAVs) is developed to collect real-time traffic information, which can be further utilized in many applications such as traffic monitoring and urban traffic management. The system includes an object detection algorithm, deep learning model training, and deployment on a real UAV. For the object detection algorithm, the Mobilenet-SSD model is applied owing to its lightweight and efficiency, which make it suitable for real-time applications on an onboard microprocessor. For model training, federated learning (FL) is used to protect privacy and increase efficiency with parallel computing. Last, the FL-trained object detection model is deployed on a real UAV for real-time performance testing. The experimental results show that the object detection algorithm can reach a speed of 18 frames per second with good detection performance, which shows the real-time computation ability of a resource-limited edge device and also validates the effectiveness of the developed system.
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Machine Learning Enhanced Real-Time Intrusion Detection Using Timing Information
Past work has investigated intrusion detection mechanisms for real-time control devices. This work contributes a novel framework of separating security monitoring and detection from real-time control, where the former is performed on Cloud edge devices while the latter is run on embedded devices attached to the system that is controlled. We contribute a security monitoring system that validates worst-case timing bounds of the target controller and also validates its control outputs by comparing it against model-based predictions, which are derived from machine learning.
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- Award ID(s):
- 1813004
- PAR ID:
- 10189506
- Date Published:
- Journal Name:
- International Workshop on Trustworthy & Real-time Edge Computing for Cyber-Physical Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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