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Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving 26

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Deformable Convolution Based Road Scene Semantic Segmentatio...

26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Manzoor, Anam Singh, Aryan Sistu, Ganesh Mohandas, Reenu Grua, Eoin Scanlan, Anthony Eising, Ciarán Data-Driven Computer Engineering Group Dept. of Electronic and Computer Engineering University of Limerick Ireland

ISBN: (纸本)9781837242672

This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios with fisheye images. These images, providing a wide field of view, pose unique challenges for extracting spatial and geometric information due to dynamic changes in object attributes. Our experiments focus on segmenting the WoodScape fisheye image dataset into ten distinct classes, assessing the Deformable Networks' ability to capture intricate spatial relationships and improve segmentation accuracy. Additionally, we explore different loss functions to address class imbalance issues and compare the performance of conventional CNN architectures with Deformable Convolution-based CNNs, including Vanilla U-Net and Residual U-Net architectures. The significant improvement in mIoU score resulting from integrating Deformable CNNs demonstrates their effectiveness in handling the geometric distortions present in fisheye imagery, exceeding the performance of traditional CNN architectures. This underscores the significant role of Deformable convolution in enhancing semantic segmentation performance for fisheye imagery. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Semantic Segmentation

Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

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Optimizing Ego Vehicle Trajectory Prediction: The Graph Enha...

IS and T International Symposium on Electronic Imaging 2024: Autonomous Vehicles and Machines, AVM 2024

作者： Sharma, Sushil Singh, Aryan Sistu, Ganesh Halton, Mark Eising, Ciarán Data-Driven Computer Engineering Group Dept. of Electronic and Computer Engineering University of Limerick Ireland

Predicting the trajectory of an ego vehicle is a critical component of autonomous driving systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) and sequential models to process front-view images for future trajectory prediction. However, these approaches often struggle with perspective issues affecting object features in the scene. To address this, we advocate for the use of Bird’s Eye View (BEV) perspectives, which offer unique advantages in capturing spatial relationships and object homogeneity. In our work, we leverage Graph Neural Networks (GNNs) and positional encoding to represent objects in a BEV, achieving competitive performance compared to traditional DNN-based methods. While the BEV-based approach loses some detailed information inherent to front-view images, we balance this by enriching the BEV data by representing it as a graph where relationships between the objects in a scene are captured effectively. © 2024, Society for Imaging Science and Technology.

关键词： Deep neural networks

Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving

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arXiv 2024年

This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios with fisheye images. These images, providing a wide field of view, pose unique challenges for extracting spatial and geometric information due to dynamic changes in object attributes. Our experiments focus on segmenting the WoodScape fisheye image dataset into ten distinct classes, assessing the Deformable Networks’ ability to capture intricate spatial relationships and improve segmentation accuracy. Additionally, we explore different loss functions to address class imbalance issues and compare the performance of conventional CNN architectures with Deformable Convolution-based CNNs, including Vanilla U-Net and Residual U-Net architectures. The significant improvement in mIoU score resulting from integrating Deformable CNNs demonstrates their effectiveness in handling the geometric distortions present in fisheye imagery, exceeding the performance of traditional CNN architectures. This underscores the significant role of Deformable convolution in enhancing semantic segmentation performance for fisheye imagery. © 2024, CC BY.

关键词： Semantic Segmentation

Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

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arXiv 2023年

作者： Sharma, Sushil Singh, Aryan Sistu, Ganesh Halton, Mark Eising, Ciarán Data-Driven Computer Engineering Group Dept. of Electronic and Computer Engineering University of Limerick Ireland

关键词： Deep neural networks

A two-sided iterative framework for model reduction of linear systems with quadratic output

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A two-sided iterative framework for model reduction of linea...

IEEE Conference on Decision and Control

作者： Ion Victor Gosea Athanasios C. Antoulas Data-Driven Reduction and Identification Group Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Electrical and Computer Engineering Rice University Houston USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

We propose a model reduction procedure for approximating large-scale linear systems with quadratic output by means of lower dimensional systems with the same structure. The framework is based on an iteration that, at each step, computes left and right projections matrices (hence two-sided). This is done by means of solving two linear Sylvester equations.

关键词： Linear systems Mathematical model Computational modeling Observability Symmetric matrices Kernel Numerical models

Case study: Approximations of the bessel function

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arXiv 2017年

作者： Karachalios, D.S. Gosea, I.V. Zhang, Q. Antoulas, A.C. Data-Driven System Reduction and Identification Group Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Electrical and Computer Engineering Rice University Houston United States

The purpose of this note is to compare various approximation methods as applied to the inverse of the Bessel function of the first kind J01(s), in a given domain of the complex plane.30E10, 03C40, 65E99 Copyright ... 详细信息

关键词： Bessel functions

Design of Blockchain based e-Voting System for Vote Requirements

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Design of Blockchain based e-Voting System for Vote Requirem...

5th International Conference on data Mining, Communications and Information Technology, DMCIT 2021

作者： Choi, Seiwoong Kang, Jihun Chung, Kwang Sik Graduate School Dept. of Information Science Korea National Open University Jongno-gu Dongsung-Dong Seoul Korea Republic of BK 21 Data-Driven Science Future Talent Education Research Group Dept of Computer Science and Engineering Korea University Seoul Korea Republic of Dept. of Computer Science Korea National Open University Jongno-gu Dongsung-Dong Seoul Korea Republic of

Voting refers to the submission of an election or an opinion on a specific matter by expressing an intention on the ballot to a certain place. The existing voting method has the burden of counting time and cost. For this reason, research has been conducted to introduce an e-voting system. However, despite the advantages of e-voting, it is not widely used due to the risk of manipulation of voting results and various requirements. Recently, in order to reduce the risk of data manipulation, research is being conducted to apply the blockchain, a technology that guarantees data integrity, to e-voting. Blockchain guarantees the integrity of data, but has a weakness in secrecy. This paper applies the critical encryption technique to the blockchain and satisfies the requirements for voting such as verifiability, anonymity, fairness, non-reusability, competence, safety, transparency, and non-ticketing. We propose a system design and implementation method. The proposed blockchain-based e-voting system provides voter anonymity by issuing a voter certificate based on a blockchain address. The e-voting election monitoring committee generates a threshold group encryption key, and the proposed blockchain-based e-voting system guarantees confidentiality by a threshold group encryption algorithm during the voting process. The voting result is encrypted through a homomorphic encryption algorithm and stored in the blockchain. Thus, the released voting results ensure safety, confidentiality, transparency, and non-vote ticketing. In addition, the proposed blockchain-based e-voting system guarantees the unity and competence of voting through the blockchain's smart contract. © Published under licence by IOP Publishing Ltd.

关键词： Transparency

On the H2 norm and iterative model order reduction of linear switched systems

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arXiv 2017年

作者： Gosea, Ion Victor Antoulas, Athanasios C. Data-Driven System Reduction and Identification Group Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Electrical and Computer Engineering Rice University Houston United States MPI Magdeburg Germany Baylor College of Medicine Houston United States

A new definition of the H2 norm for linear switched systems is introduced. It is based on appropriately defined time-domain kernels, or equivalently, on infinite controllability and observability Gramian matrices. Furthermore, an extension of the iterative rational Krylov algorithm to the class of linear switched systems is proposed.93A15, 93A30, 93B05, 93B07, 93B11, 93C10, 93C15 Copyright © 2017, The Authors. All rights reserved.

关键词： Iterative methods

Approximation of a damped Euler-Bernoulli beam model in the Loewner framework

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arXiv 2017年

作者： Gosea, I.V. Antoulas, A.C. Data-Driven System Reduction and Identification Group Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstrasse 1 Magdeburg39106 Germany Department of Electrical and Computer Engineering Rice University 6100 Main St MS-366 HoustonTX77005 United States

The Loewner framework for model order reduction is applied to the class of infinite-dimension systems. The transfer function of such systems is irrational (as opposed to linear systems, whose transfer function is rational) and can be expressed as an infinite series of rational functions. The main advantage of the method is the fact that reduced orders models are constructed using only input-output measurements. The procedure can be directly applied to the original transfer function or to the one obtained from the finite element discretization of the PDE. Significantly better results are obtained when using it directly, as it is presented in the experiments section.93A30, 93B15, 93C15, 93C20 Copyright © 2017, The Authors. All rights reserved.

关键词： Transfer functions