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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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Robustness in trajectory prediction for autonomous vehicles: a survey

Robustness in trajectory prediction for autonomous vehicles:...

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IEEE Symposium on Intelligent Vehicle

作者： Jeroen Hagenus Frederik Baymler Mathiesen Julian F. Schumann Arkady Zgonnikov Department of Cognitive Robotics TU Delft Delft The Netherlands Delft Center for Systems and Control TU Delft Delft The Netherlands

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or suboptimal behavior. To address these challenges, this paper presents a comprehensive framework that categorizes and assesses the definitions and strategies used in the literature on evaluating and improving the robustness of trajectory prediction models. This involves a detailed exploration of various approaches, including data slicing methods, perturbation techniques, model architecture changes, and post-training adjustments. In the literature, we see many promising methods for increasing robustness, which are necessary for safe and reliable autonomous driving.

关键词： Surveys Navigation Perturbation methods Decision making Predictive models Particle measurements Robustness

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control of Continuum Manipulators with Shape Constraints via Deep Reinforcement Learning

Control of Continuum Manipulators with Shape Constraints via...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Nima Maghooli Omid Mahdizadeh Mohammad Bajelani S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Continuum robots, while versatile for handling complex tasks, present significant challenges in control system design. This paper introduces a novel framework that integrates position and orientation control laws to address shape constraints effectively. Specifically, a Deep Reinforcement Learning (DRL) strategy is proposed to facilitate trajectory tracking within the desired orientation. To ensure safety, the framework directly controls the end-effector's position and orientation in the workspace, avoiding unsafe zones. A centralized control law, the Modified Transpose Jacobian (MTJ), is employed to resolve the robot's redundancy without relying on inverse kinematics. The proposed approach is validated through simulations on a Tendon-Driven Continuum Robot (TDCR), demonstrating superior performance compared to similar learning-based controllers.

关键词： Meters Mechatronics Shape Trajectory tracking Termination of employment Position control Kinematics Deep reinforcement learning Safety Continuum robots

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Real-Time Face-Tracking control of a 4 DOF Manipulator Using Deep Learning Detection

Real-Time Face-Tracking Control of a 4 DOF Manipulator Using...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Ali Asadi Amirreza Hassan Nejad S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This paper has presented the design, simulation, and real-time implementation of a four-degree-of-freedom (4-DOF) robotic system for face detection and tracking. The system has combined kinematic control with deep learning. It has used the Xception network for high-accuracy facial detection. A Fuzzy PID-MIJ control algorithm has optimized the trajectory planning. This has ensured smooth and precise target tracking. Both simulations and physical experiments have validated the system's real-time performance. The results have shown that the system can reliably track faces with minimal error. This approach has demonstrated significant potential for dynamic, human-interactive applications.

关键词： Deep learning Target tracking Trajectory planning Target recognition Face recognition Kinematics Real-time systems Robustness Planning Face detection

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Detection-segmentation convolutional neural network for autonomous vehicle perception

arXiv

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arXiv 2023年

作者： Baczmanski, Maciej Synoczek, Robert Wasala, Mateusz Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-performance computing platforms. In the case of autonomous vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms with limited computing power, which makes it difficult to meet the requirements described above. A reduction in the complexity of the network can be achieved by using an appropriate: architecture, representation (reduced numerical precision, quantisation, pruning), and computing platform. In this paper, we focus on the first factor – the use of so-called detection-segmentation networks as a component of a perception system. We considered the task of segmenting the drivable area and road markings in combination with the detection of selected objects (pedestrians, traffic lights, and obstacles). We compared the performance of three different architectures described in the literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments on a custom dataset consisting of approximately 500 images of the drivable area and lane markings, and 250 images of detected objects. Of the three methods analysed, MultiTask V3 proved to be the best, achieving 99% mAP50 for detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane segmentation, as well as 124 fps on the RTX 3060 graphics card. This architecture is a good solution for embedded perception systems for autonomous vehicles. The code is available at: https://***/vision-agh/MMAR_2023. © 2023, CC BY.

关键词： Autonomous vehicles

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Detection-segmentation convolutional neural network for autonomous vehicle perception

TechRxiv

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TechRxiv 2023年

作者： Baczmanski, Maciej Synoczek, Robert Wasala, Mateusz Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

关键词： Autonomous vehicles

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High-definition event frame generation using SoC FPGA devices

TechRxiv

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TechRxiv 2023年

作者： Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University Krakow Poland

In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD – 1280 × 720 pixels) onto the image plane in FPGA devices. The results confirm the feasibility of this approach, but there are a number of challenges, limitations and trade-offs to be considered. The required hardware resources of selected data representations, such as binary frame, event frame, exponentially decaying time surface and event frequency, were compared with those available on several popular platforms from AMD Xilinx. The resulting event frames can be used for typical vision algorithms, such as object classification and detection, using both classical and deep neural network methods. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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High-definition event frame generation using SoC FPGA devices

arXiv

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arXiv 2023年

作者： Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

关键词： System-on-chip

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High-definition event frame generation using SoC FPGA devices

High-definition event frame generation using SoC FPGA device...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Krzysztof Blachut Tomasz Kryjak Department of Automatic Control and Robotics Embedded Vision Systems Group AGH University of Krakow Poland

In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD – 1280×720 pixels) onto the image plane in FPGA devices. The results confirm the feasibility of this approach, but there are a number of challenges, limitations and trade-offs to be considered. The required hardware resources of selected data representations, such as binary frame, event frame, exponentially decaying time surface and event frequency, were compared with those available on several popular platforms from AMD Xilinx. The resulting event frames can be used for typical vision algorithms, such as object classification and detection, using both classical and deep neural network methods.

关键词：

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Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms

arXiv

引用

arXiv 2023年

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

Object detection in 3D is a crucial aspect in the context of autonomous vehicles and drones. However, prototyping detection algorithms is time-consuming and costly in terms of energy and environmental impact. To address these challenges, one can check the effectiveness of different models by training on a subset of the original training set. In this paper, we present a comparison of three algorithms for selecting such a subset – random sampling, random per class sampling, and our proposed MONSPeC (Maximum Object Number Sampling per Class). We provide empirical evidence for the superior effectiveness of random per class sampling and MONSPeC over basic random sampling. By replacing random sampling with one of the more efficient algorithms, the results obtained on the subset are more likely to transfer to the results on the entire dataset. The code is available at: https://***/vision-agh/monspec. © 2023, CC BY.

关键词： Object detection

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