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Real-Time Multi-object Tracking Using YOLOv8 and SORT on a SoC FPGA 21st

Real-Time Multi-object Tracking Using YOLOv8 and SORT on a...

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21st International Symposium on Applied Reconfigurable Computing, ARC 2025

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031879944

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on Computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on Computer Vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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Accuracy and cross-sensitivity analysis of the PMS5003 formaldehyde sensor 27

Accuracy and cross-sensitivity analysis of the PMS5003 forma...

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27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Bronczyk, Karolina Adamski, Michal Dabrowska, Agata Konieczka, Adam Dabrowski, Adam Adam Mickiewicz University Faculty of Chemistry Department of Trace Analysis Poznań Poland Poznan University of Technology Division of Electronic Systems and Signal Processing Institute of Automatic Control and Robotics Poznań Poland

ISBN: (纸本)9788362065486

Measurement data produced with the PMS5003 electrochemical sensor, allegedly selective for formaldehyde, were analyzed. The amounts of formaldehyde released from heated standard solutions were measured. At the same time, samples were sorbed into sorption tubes with an XAD-2 cartridge, which enabled chromatographic analysis - gas chromatography coupled with an electron capture detector (GC-ECD). A susceptibility of the PMS5003 sensor to cross-sensitivity associated with the presence of other low-molecular-weight carbonyl compounds, such as acetaldehyde and acetone in the sample, was also tested. The conducted research clearly indicates poor selectivity/accuracy of the PMS5003 sensor in relation to formaldehyde and its sensitivity to the presence of other carbonyl compounds in the sample, with a structure similar to formaldehyde. © 2024 Division of Signal Processing and Electronic systems, Poznan University of technology (DSPES PUT).

关键词： Acetone

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Electrochemical determination of pollutants from various passenger car engines 27

Electrochemical determination of pollutants from various pas...

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27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

ISBN: (纸本)9788362065486

This paper presents measurements of the concentrations of particulate matter (PM1, PM2.5, and PM10), carbon dioxide (CO2), total organic compounds (TVOCs), and formaldehyde as pollutants released during fuel combustion. For this purpose, a suitable measuring device containing selective electrochemical sensors was prepared. The influence of fuel type and engine speed on the amount of pollutants released into the environment, was studied. Based on the achieved results, it can be concluded that the factor influencing the amount of pollutants released is mainly the type of fuel. Gasoline-powered cars can pollute the environment in a particularly negative way. A relationship was also noted between the increase in engine speed and the concentration of released carcinogenic formaldehyde. © 2024 Division of Signal Processing and Electronic systems, Poznan University of technology (DSPES PUT).

关键词： Electrochemical sensors

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Two approaches (GC-ECD and electrochemical sensors signals processing) to the determination of carbonyl compounds as markers of air pollution 26

Two approaches (GC-ECD and electrochemical sensors signals p...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Bronczyk, Karolina Adamski, Michal Dabrowska, Agata Konieczka, Adam Dabrowski, Adam Adam Mickiewicz University Faculty of Chemistry Department of Trace Analysis Poznań Poland Institute of Automatic Control and Robotics Poznan University of Technology Division of Signal Processing and Electronic Systems Poznań Poland

ISBN: (纸本)9798350304985

This article presents the application possibilities of unmanned aerial vehicles (UAVs) for the rapid and efficient determination of carbonyl compounds: formaldehyde and acetaldehyde concentrations in gaseous environmental samples. One of the described methods is the use of special electrochemical sensors (PMS5003 and SEN0231) to measure air pollution in real time (every 10 seconds). The second method involves adsorbing gas samples into sorption tubes with XAD-2 cartridges and uses chromatographic analysis: gas chromatography coupled with a selective electron capture detector (GC-ECD). The proposed analytical technique allows the determination of carbonyl compounds at low concentration levels (μg/m3) using pre-derivatization of samples with the reagent 2,3,4,5,6-pentafluorobenzyl-hydroxylamine (PFBOA). Comparing the results obtained from electrochemical sensors and chromatographic analysis, we can conclude that there are discrepancies between the determined concentrations of formaldehyde in the analyzed gas samples. © 2023 Division of Signal Processing and Electronic systems, Poznan University of technology (DSPES PUT).

关键词： Formaldehyde

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PointPillars Backbone Type Selection for Fast and Accurate LiDAR Object Detection

PointPillars Backbone Type Selection for Fast and Accurate L...

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International Conference on Computer Vision and Graphics, ICCVG 2022

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031220241

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars-backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Robust Sliding Mode control of a Quadrotor With Disturbance Rejection for Enhanced Stability and Performance 6

Robust Sliding Mode Control of a Quadrotor With Disturbance ...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Ullah, Hameed Zarin, Owais Ahmad, Irfan Ahmad, Sarfraz Abbas, Ansar Lippiello, Vincenzo University of Naples Federico II Department of Electrical Engineering and Information Technology Naples Italy Riphah International University Department of Electrical Engineering Islamabad Pakistan School of Bilbao - EIB/BIE University of the Basque Country - UPV/EHU Automatic Control Group ACG Department of Automatic Control and Systems Engineering Rafael Moreno 3 Bilbao48013 Spain CEME Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

This research presents a comprehensive analysis of quadrotor stabilization and trajectory tracking control using Proportional-Integral-Derivative (PID) and Sliding Mode control (SMC) with integrated disturbance rejection. The nonlinear dynamics of the quadrotor are formulated using the Newton-Euler approach, accounting for six degrees of freedom. The PID controller is implemented as a baseline, while the SMC is designed for enhanced robustness to model uncertainties and external perturbations, with a disturbance observer for managing time-varying disturbances. A rigorous Lyapunov stability proof ensures global asymptotic stability of the SMC-controlled system. Extensive simulations evaluate both controllers in terms of tracking accuracy, control effort, and robustness to external disturbances. Results show that SMC outperforms PID in dis-turbance rejection, tracking accuracy, and robustness, making it ideal for UAV applications in complex environments. © 2024 IEEE.

关键词： Sliding mode control

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Traffic Sign Detection With Event Cameras and DCNN 25

Traffic Sign Detection With Event Cameras and DCNN

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25th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2022

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Agh University of Science and Technology Computer Vision Laboratory Department of Automatic Control and Robotics Kraków Poland Silesian University of Technology Department of Digital Systems Gliwice Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions - parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022 Division of Signal Processing and Electronic systems, Poznan University of technology (DSPES PUT).

关键词： Cameras

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Hydrodynamic and Static Stability analysis of a Hybrid Offshore Wind-Wave Energy Generation: An Expansion of Semisubmersible Floating Wind Turbine Concept 15th

Hydrodynamic and Static Stability Analysis of a Hybrid Offsh...

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15th European Wave and Tidal Energy Conference, EWTEC 2023

作者： Aboutalebi, Payam Garrido, Aitor J. Garrido, Izaskun Nguyen, Dong Trong Gao, Zhen Department of Marine Technology Trondheim7491 Norway Automatic Control Group–ACG Institute of Research and Development of Processes–IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao University of the Basque Country–UPV/EHU Spain School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

Marine structures like Floating Wind Turbine (FWT) is exposed to the oncoming waves and wind that can cause oscillatory motions within the system. These undesired oscillations can have negative impacts on the efficiency of the system, reduce its lifespan, hinder energy extraction, increase stress levels, and raise maintenance costs. To mitigate these negative impacts, the integration of Wave Energy Converters (WECs) into the FWT system has been proposed. This hybrid system may be capable of extracting coupled wind-wave energy and transferring electrical power to the shared grid. This paper presents an investigation of the use of Oscillating Water Columns (OWCs), a type of WECs, within a FWT system. The purpose of using an OWC is to increase the hydrodynamic damping and reduce the resonant motions of the floating wind turbines under environmental loads, including both wind and wave loads. This is because the wave energy from OWC would be very small as compared to the wind energy. However, OWCs can provide a damping source for reducing the resonant motions of the floater, especially the pitch resonant motions. This would be very beneficial for the power performance of the floating w ind turbine and the structural design of the floater. T he p urpose of this paper is to redesign the original FWT platform to accommodate the additional OWCs by considering the hydrostatic stability and hydrodynamics since the new elements, the OWCs, can significantly change the response of the platform. The redesign of the original FWT involves the integration of OWCs within two out of three columns of an existing semisubmersible platform for a 12 MW FWT. To do this, two moonpools, which are consistent with OWC air chambers, have been created within two columns of the FWT. The water ballast was designed for the columns with and without OWCs. After that the redesign is done hydrostatic stability and hydrodynamics analyses are evaluated. The hydrodynamics properties are discussed in terms o

关键词： Tidal power

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