Obermaisser, Roman
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Obermaisser, Roman
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Publication Access status: Metadata only , FPGA-Based Real-Time Object Detection and Classification System Using YOLO for Edge ComputingThe leap forward in research progress in real-time object detection and classification has been dramatically boosted by including Embedded Artificial Intelligence (EAI) and Deep Learning (DL). Real-time object detection and classification with deep learning require many resources and computational power, which makes it more difficult to use deep learning methods on edge devices. This paper proposed a new, highly efficient Field Programmable Gate Array (FPGA) based real-time object detection and classification system using You Only Look Once (YOLO) v3 Tiny for edge computing. However, the proposed system has been instantiated with Advanced Driving Assistance Systems (ADAS) for evaluation. Traffic light detection and classification are crucial in ADAS to ensure drivers’ safety. The proposed system used a camera connected to the Kria KV260 FPGA development board to detect and classify the traffic light. Bosch Small Traffic Light Dataset (BSTLD) has been used to train the YOLO model, and Xilinx Vitis AI has been used to quantify and compile the YOLO model. The proposed system can detect and classify traffic light signals from a high-definition (HD) video streaming in 15 frames per second (FPS) with 99% accuracy. In addition, it consumes only 3.5W power, demonstrating the ability to work on edge devices. The on-road experimental results represent fast, precise, and reliable detection and classification of traffic lights in the proposed system. Overall, this paper demonstrates a low-cost and highly efficient FPGA-based system for real-time object detection and classification.Source Type:Article5 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Metascheduling Using Discrete Particle Swarm Optimization for Fault Tolerance in Time-Triggered IoT-WSNIn time-triggered systems, adaptation is performed by using metascheduling approaches to ensure temporal predictability. Wireless sensor networks (WSNs) are growing to be used as a reliable, an energy-efficient, and a scalable network infrastructure for numerous Internet of Things (IoT) applications. Because of run-time changes because of failure events, a metascheduling system is required to address the changes by precomputing schedules for several context events at design time. Therefore, this paper proposes a metascheduler that solves a new scheduling problem for each adaptation scenario in IoT-WSN using our offline algorithm named discrete particle swarm optimization for reliable task allocation (DPSO-TA), resulting in a multi-schedule graph that combines the repeated schedules. In this work, a time-triggered IoT-WSN metascheduler is proposed that takes into considerations the node or link failures that are common in WSN. Single and two failure events are considered, where a graph for all feasible schedules is formed at the network design time. Such a large number of schedules causes a state space explosion problem, which is managed using a re-convergence method. Different application model sizes and network topologies are used to assess the proposed metascheduler under various failure event scenarios. The results show a reduction in the size of the multi-schedule graph by applying inputs with different deadline values and different number of tasks. Furthermore, the validity suggested by the proposed metascheduler, which is the ratio of valid to invalid schedules, is improved when compared to algorithms that schedule tasks to hosts with the shortest completion time or algorithms that select hosts for a set of sorted tasks based on energy consumed and task arrival time.Source Type:Article1
