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Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

Jigsaw-based Benchmarking for Learning Robotic Manipulation

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arXiv 2023年

作者： Liu, Xiaobo Wan, Fang Ge, Sheng Wang, Haokun Sun, Haoran Song, Chaoyang Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong Department of Computer Science The University of Hong Kong Hong Kong

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence. © 2023, CC BY.

关键词： Benchmarking

Optimization Design for Federated Learning in Heterogeneous 6G Networks

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arXiv 2023年

作者： Luo, Bing Ouyang, Xiaomin Sun, Peng Han, Pengchao Ding, Ningning Huang, Jianwei The Data Science Research Center the Division of Natural and Applied Sciences Duke Kunshan University Jiangsu Kunshan China The department of Information Engineering The Chinese University of Hong Kong Hong Kong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for innovative applications and intelligent services. However, traditional machine learning (ML) approaches require centralizing the training data in the data center or cloud, raising serious user-privacy concerns. Federated learning, as an emerging distributed AI paradigm with privacy-preserving nature, is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. However, there are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenging heterogeneity issues from three aspects: incentive mechanism design, network resource management, and personalized model optimization. We also present some open problems and promising directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Reputation-based resilient consensus with privacy guarantees

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IEEE Transactions on Control of Network Systems 2025年

作者： Ramos, Guilherme Pequito, Sergio Silvestre, Daniel University of Lisbon Dept. of Computer Science and Engineering Instituto Superior Técnico Portugal Instituto de Telecomunicações Lisbon1049-001 Portugal Universidade de Lisboa Dept. of Electrical and Computer Engineering Instituto Superior Técnico Lisbon1049-001 Portugal University of Lisbon Institute for Systems and Robotics Instituto Superior Técnico Portugal NOVA University of Lisbon FCT/UNL School of Science and Technology Caparica2829-516 Portugal COPELABS Lusófona University Portugal

Reputation-based consensus methods have gained significant attention in distributed systems to mitigate the impact of malicious agents and ensure reliable decision-making. However, privacy concerns arise when sensitive information is shared among participants. In this paper, we propose a reputation-based consensus method that incorporates resilience and privacy guarantees, providing a balance between preserving participants' privacy and maintaining the accuracy of the resilient consensus process. Our approach leverages the notion of privacy to add noise to the agents' states, ensuring that individual contributions are masked while still enabling the detection of malicious behavior. We present a detailed analysis of the privacy-accuracy trade-off and demonstrate the effectiveness of our method through simulations. The results show that our reputation-based consensus method with privacy guarantees offers robustness against attacks while preserving the privacy of participants, making it a promising solution for privacy-conscious resilient distributed systems. © 2014 IEEE.

关键词： Sensitive data

Integrating a Digital Twin Concept in the Zero Emission Sea Transporter (ZEST) Project for Sustainable Maritime Transport Using Stonefish Simulator

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Integrating a Digital Twin Concept in the Zero Emission Sea ...

OCEANS

作者： Michele Grimaldi Carlo Cernicchiaro George Rossides Angelos Ktoris Elias Yfantis Ioannis Kyriakides Computer Vision and Robotics Institute University of Girona Girona Spain School of Engineering & Physical Sciences Heriot-Watt University Marine Robotics Innovation Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Computational Cognition Lab Open University of Cyprus Nicosia Cyprus Marine & Offshore Science Technology & Engineering Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Department of Engineering University of Nicosia Nicosia Cyprus Marine Technology Division Cyprus Marine and Maritime Institute Larnaca Cyprus

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

In response to stringent emission reduction targets imposed by the International Maritime Organization (IMO) and the European Green Deal's Fit for 55 legislation package, the maritime industry has shifted its focus towards decarbonization. While significant attention has been placed on vessels exceeding 5,000 gross tons (GT), emissions from coastal and short sea shipping, amounting to approximately 13% of global shipping transportation and 15% within the European Union (EU), have not received adequate consideration. This abstract introduces the Zero Emission Sea Transporter (ZEST) project, designed to address this issue by developing a zero-emissions multi-purpose catamaran for short sea routes, shown in Figure 1.

关键词： Visualization Scalability Europe Sea measurements Seaports Low carbon economy Digital twins

A loading path planning method for co-loading in automatic harvesting system

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A loading path planning method for co-loading in automatic h...

International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Xiaoxiong Wang Zhuo Wang Xiaoping Bai Zhikang Ge Jian Wang Zhihong Man Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne Australia

In this paper, a path planning method is developed for co-loading in master-slave automatic harvesting. It is shown that, the adjustment of the unloading port position relative to the grain tank can be performed jointly by the longitudinal relative position between the vehicles and the angle of the unloading arm. Then the loading path based on the Hamiltonian circuit is designed to guide the motion of the unloading port during the loading process. By improving the loading uniformity, the loading path proposed can maximize the capacity utilization of grain tank which can reduce the round-trip frequency of the transport vehicle and improve the energy and time efficiency of the harvesting operation. Simulation results are provided to validate the excellent performance of presented path planning method.

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Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

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arXiv 2024年

作者： Lin, Fangzhou Liu, Haotian Zhou, Haoying Hou, Songlin Yamada, Kazunori D. Fischer, Gregory S. Li, Yanhua Zhang, Haichong K. Zhang, Ziming Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Dell Technologies HopkintonMA01748 United States Graduate School of Information Sciences Tohoku University Sendai980-8579 Japan Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://***/Zhang-VISLab/ IROS2024-LossDistillationWeightedCD. © 2024, CC0.

关键词： Deep learning

Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

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arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

Visual Exploration-Enhanced Quadruped Robot with Active-Passive Composite Telescope Mechanism

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Visual Exploration-Enhanced Quadruped Robot with Active-Pass...

Intelligent robotics and Automatic Control (IRAC), International Conference on

作者： Xuchen Wang Shengzhi Wang Yiying Liang Jiajun An Puchen Zhu Weibing Li Jianshu Zhou Xin Ma Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Division of Advanced Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Agriculture and Biological Engineering Purdue University West Lafayette USA School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mechanical Engineering University of California Berkeley USA Multi-Scale Medical Robotics Center Hong Kong China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

Navigating hazardous environments and confined spaces during disaster search and rescue operations poses significant challenges to both human rescuers and conventional robotic systems. Recognizing the need for enhanced capabilities, we introduce a Visual Exploration-Enhanced Quadruped Robot (VEQR), a novel platform specifically designed to improve adaptability and exploration in such demanding scenarios. The VEQR combines a quadruped robot with an innovative active-passive composite telescopic mechanism—a compact, 12.8 mm diameter device equipped with a camera—that enables maneuvering through narrow and curved spaces like slits and debris-filled areas. The bending and insertion/retraction capabilities of this mechanism improve the dexterity of the robot in confined spaces and extend its reach into deeper, hard-to-access zones. Experimental results demonstrate the VEQR’s proficiency in overcoming obstacles, inspecting confined spaces, and providing clear visual feedback. Notably, the VEQR successfully traversed narrow and winding passages as small as 50 mm diameter and inspected targets positioned up to 0.6 mm deep within confined spaces, confirming its effectiveness as a robust robot for search and rescue operations in hazardous environments.

关键词： Visualization Navigation Disasters Windings Telescopes Bending Inspection Robustness Quadrupedal robots