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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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LiFT: Lightweight, FPGA-Tailored 3D Object Detection Based on LiDAR Data 18th

LiFT: Lightweight, FPGA-Tailored 3D Object Detection Based o...

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18th International Workshop on Design and Architecture for Signal and Image Processing, DASIP 2025

作者： Lis, Konrad Kryjak, Tomasz Gorgoń, Marek Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031878961

This paper presents LiFT, a lightweight, fully quantized 3D object detection algorithm for LiDAR data, optimized for real-time inference on FPGA platforms. Through an in-depth analysis of FPGA-specific limitations, we identify a set of FPGA-induced constraints that shape the algorithm’s design. These include a computational complexity limit of 30 GMACs (billion multiply-accumulate operations), INT8 quantization for weights and activations, 2D cell-based processing instead of 3D voxels, and minimal use of skip connections. To meet these constraints while maximizing performance, LiFT combines novel mechanisms with state-of-the-art techniques such as reparameterizable convolutions and fully sparse architecture. Key innovations include the Dual-bound Pillar Feature Net, which boosts performance without increasing complexity, and an efficient scheme for INT8 quantization of input features. With a computational cost of just 20.73 GMACs, LiFT stands out as one of the few algorithms targeting minimal-complexity 3D object detection. Among comparable methods, LiFT ranks first, achieving an mAP of 51.84% and an NDS of 61.01% on the challenging NuScenes validation dataset. The code will be available at https://***/vision-agh/lift. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3d object detection FPGA LiDAR NuScenes quantisation

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Real-Time Multi-Object Tracking using YOLOv8 and SORT on a SoC FPGA

arXiv

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arXiv 2025年

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

关键词： System-on-chip

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LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR data

arXiv

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arXiv 2025年

作者： Lis, Konrad Kryjak, Tomasz Gorgoń, Marek Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

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Vision-based automatic fruit counting with UAV

arXiv

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arXiv 2025年

作者： Szolc, Hubert Wasala, Mateusz Mietla, Remigiusz Iwicki, Kacper Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow al. Mickiewicza 30 Krakow30–059 Poland

The use of unmanned aerial vehicles (UAVs) for smart agriculture is becoming increasingly popular. This is evidenced by recent scientific works, as well as the various competitions organised on this topic. Therefore, in this work we present a system for automatic fruit counting using UAVs. To detect them, our solution uses a vision algorithm that processes streams from an RGB camera and a depth sensor using classical image operations. Our system also allows the planning and execution of flight trajectories, taking into account the minimisation of flight time and distance covered. We tested the proposed solution in simulation and obtained an average score of 87.27/100 points from a total of 500 missions. We also submitted it to the UAV Competition organised as part of the ICUAS 2024 conference, where we achieved an average score of 84.83/100 points, placing 6th in a field of 23 teams and advancing to the finals. © 2025, CC BY.

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An image processing-based method for determining the geometry transmission ratio of a continuously variable transmission gearbox

An image processing-based method for determining the geometr...

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International Symposium on Applied Science 2024, ISAS 2024

作者： Tran, Dang Long Do, Tri Nhut 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Department of Embedded Systems and Robotics Faculty of Computer Engineering University of Information Technology Thu Duc City Ho Chi Minh City71308 Viet Nam Vietnam National University in Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City71308 Viet Nam

Nowadays, many applications based on image processing methods are deployed to bring convenience to use due to its outstanding superior features. This paper proposes an image processing-based method for determining the geometry transmission ratio of a continuously variable transmission (CVT) gearbox made up of a push-belt and two pulleys with synchronized width adjustments. The proposed method is demonstrated by many experiments on a real system consisting of 1 Logitech C992 Pro Webcam camera and 1 CVT gearbox. The experiments were conducted on more than 100 images. The experimental results show that the method's error is between 6% and 8%, and the average processing time of 1 image is 0.07 seconds. This error depends on whether the camera position is parallel to the CVT gearbox's cross-sectional plane or not. Furthermore, the reliability of the method is evaluated by the RMSE method up to 99%. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Gears

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial Intelligence Autonomous Vehicles Deep leaning Edge Computing Internet of Vehicles

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence Computer architecture Data models Accuracy Servers Safety

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Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

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IFAC-PapersOnLine 2017年第1期50卷 5774-5781页

作者： Magdici S. Althoff M. Department of Robotics and Embedded Systems Technical University of Munich Germany

This paper addresses the problem of following a vehicle with varying acceleration in a comfortable and safe manner. Our architecture consists of a nominal controller (here: model predictive control) and a safety controller. Although model predictive control attempts to keep a safe distance, it cannot formally guarantee it, due to the assumptions on the behavior of the leading vehicle. We address this problem by holding a formally verified safety controller available. Our novel mechanism gradually engages the safety maneuver since most critical situations resolve quickly. The overall approach is evaluated against real traffic data. The results show good position and velocity tracking performance, while safety and comfort are guaranteed. © 2017

关键词： Autonomous vehicles Intelligent cruise control Safety guarantees

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Template-based development of fault-tolerant embedded software

Template-based development of fault-tolerant embedded softwa...

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2006 International Conference on Software Engineering Advances, ICSEA'06

作者： Buckl, Christian Knoll, Alois Schrott, Gerhard Robotics and Embedded Systems Department of Informatics Technische Universität München

ISBN: (纸本)0769527035

Currently there are different approaches to develop fault-tolerant embedded software: implementing the system from scratch or using libraries respectively specialized hardware. By implementing from scratch the developer has all options concerning system design, the used programming language and hardware. But on the other hand the implementation is error-prone and time- and cost-intensive. The usage of libraries or specialized hardware reduces the design possibilities, while increasing the quality of the developed system and accelerating the development. We present a new technique for developing fault-tolerant systems that combines the advantages of these approaches. We suggest the implementation of reusable templates that solve different aspects of fault-tolerant systems, for example temporal synchronization. In addition we introduce a code generator that realizes a mapping of these templates into application-dependent source code. © 2006 IEEE.

关键词： Fault tolerant computer systems

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