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YOLOv5s-SLAM: A Monocular Visual SLAM method in Dynamic Environments

YOLOv5s-SLAM: A Monocular Visual SLAM method in Dynamic Envi...

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International Conference on computer and Communications (ICCC)

作者： Zhenyu Yin Dan Feng Chao Fan Xiaohui Wang Chinese Academy of Sciences Shenyang Institute of Computing Technology Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware & Software Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences

To deal with the problem that traditional visual SLAM methods in dynamic environments are susceptible to the influence of moving targets, which leads to a decrease in the system's localization accuracy. This paper presents a dynamic visual SLAM system named YOLOv5s-SLAM. YOLOv5s is embedded in the ORB-SLAM3 system to recognize dynamic targets in the surrounding environment by deep learning methods and simultaneously extract ORB feature points with the SLAM system at the front end as a way to improve real-time performance. Combined with the constructed dynamic object detection rules, the dynamic feature points are divided and removed, which effectively reduces the mismatching in the feature extraction process and the impact on the system performance, and achieves the optimization of the visual SLAM localization performance. Tests on the TUM dataset show that the proposed method has smaller trajectory errors and higher position estimation accuracy, which can effectively enhance the robustness of localization.

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Uncertainty-Guided Pixel-Level Contrastive Learning for Biomarker Segmentation in OCT Images 17th

Uncertainty-Guided Pixel-Level Contrastive Learning for Biom...

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17th International Conference on Intelligent computing, ICIC 2021

作者： Bai, Yingjie Liu, Xiaoming Li, Bo Zhou, Kejie School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China

ISBN: (纸本)9783030845285

Optical coherence tomography (OCT) has been widely leveraged to assist doctors in clinical ophthalmic diagnosis, since it can show the hierarchical structure of the retina. The type and size of biomarkers are crucial in the classification and grading of diseases. Hence, automatic segmentation of biomarkers is important to quantitative analysis, which can reduce a heavy workload. In this paper, we propose a novel deep learning-based method for biomarker segmentation on OCT images. The contrastive learning is introduced to enhance the contextual relationship between pixels in the dataset instead of just in an image. In addition, uncertainty is used to weight the segmentation loss to prompt the network focus on the learning of hard pixels. At the same time, uncertainty is utilized to select hard pixels for guiding network to perform contrastive learning, which makes the segmentation result more accurate. The experiment results evaluated on a local dataset, demonstrate the effectiveness of the proposed biomarker segmentation framework. © 2021, Springer Nature Switzerland AG.

关键词： Biomarkers

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A Continuous Verification Mechanism for Clients in Federated Unlearning to Defend the Right to be Forgotten

A Continuous Verification Mechanism for Clients in Federated...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Shanshan Chen Jun Liu Yuying Li Yamin Li Yang Yang Gaoyang Liu Chen Wang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In Federated Learning (FL), the regulatory need for the "right to be forgotten" requires efficient Federated Unlearning (FU) methods, which enable FL models to unlearn appointed training data. Associating with the emergence FU, verifying the performance of FU plays a critical role in evaluating the consistency FU methods, in case of the unexpected degradation of the FL model. Though well developed, none of the existing verification methods in FU stands for the clients who opt out of the FL process, which is a universal demand in FL. More specifically, after the clients quit the FL cooperation, they can no longer verify whether the FL model unlearns their data after the FL keeps training for several rounds. To this end, we introduce a continuous verification mechanism for FL clients, called Backdoor Attack-based Forgetting Verification (BAFV). Inspired by backdoor attack, BAFV embedded a persistent mark for the client that proposes to leave, with the intention that the client still has the right to verify of FU after leaving the FL cooperation for a relatively long period. Extensive experiments across diverse FU environments and datasets demonstrated that our method maintains the accuracy of model and provides clients with a continuous verification mechanism to defend their rights. Our code of BAFV is publicly available at: https://***/paper-liu/***.

关键词： Training Privacy Distributed processing Accuracy Federated learning Training data Feature extraction Data models Servers Protection

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Prediction model of sleep membership in drugstores based on immune danger theory

Prediction model of sleep membership in drugstores based on ...

引用

2021 IEEE International Conference on Power Electronics, computer Applications, ICPECA 2021

作者： Yan, Song Li, Tao Zheng, Haotian Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

ISBN: (纸本)9781728190037

The artificial immune danger theory is introduced into the analysis of user behavior characteristics to effectively identify the dormant member users of pharmacies. Take pharmacy members as an example, analyze the behavior characteristics of pharmacy members, select characteristic attributes such as purchasing behavior, medical history, gender and age, payment method, activity level, membership card points, *** the attribute analysis result as the characteristic signal to distinguish between quasi-sleeping member users and normal users, and based on Dendritic Cells Algorithm(DCA) to realize the identification of quasi-sleeping members of *** effectiveness of the algorithm is verified and compared with the real data of pharmacy member users. The results show that the method can effectively detect users who may become sleeping members among pharmacy members, and has a high detection accuracy rate. © 2021 IEEE.

关键词： Behavioral research

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Design and implementation of intelligent question and answer electronic dictionary in the context of big data

Design and implementation of intelligent question and answer...

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Communications technology and computer science (ACCTCS), Asia-Pacific Conference on

作者： Rui Qiao Xiaolei Zhong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China Yunnan University Dianchi College Kunming China

In the current era of the Internet, social life is inseparable from data, and people also live in a network. For people, network technology and smartphones are like swimming fish without water. The swimming fish, a pond obviously cannot meet the demand, people need the sea, and the development of science and technology is as broad as the sea and has unlimited future development space [1]-[4]. The Android system is the main part of the mobile smart phone system. For this, I have developed an English-Chinese electronic dictionary app based on android. It realizes the functions of registration, login, vocabulary query, input translation, and new words of dictionary on the platform. In order to take into account various factors and user needs, the software interface design is concise and clear, and the use and operation are convenient and easy to understand. All functions are operated according to the preset plan, and the project functions are fully realized, except for some functions that cannot be completed due to technical reasons or other force majeure factors [5]-[6]. The software platform can run normally on the Android system smart terminal. After installing the software, users can use it to query English vocabulary in a normal network environment. On this basis, speech recognition translation and photo recognition translation are also added. The basic knowledge and technology of android can be used to quickly complete the production and design of the ui. The Android electronic dictionary app is based on the Android electronic dictionary app. After running the test work on various Android mobile devices and emulators, the requirements design is debugged, and the concept of the software is explained. The main significance of the design and its specific implementation process is to allow more Android users to fully enjoy and understand the software running under the android platform.

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3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack

3DHacker: Spectrum-based Decision Boundary Generation for Ha...

引用

International Conference on computer Vision (ICCV)

作者： Yunbo Tao Daizong Liu Pan Zhou Yulai Xie Wei Du Wei Hu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wangxuan Institute of Computer Technology Peking University

With the maturity of depth sensors, the vulnerability of 3D point cloud models has received increasing attention in various applications such as autonomous driving and robot navigation. Previous 3D adversarial attackers either follow the white-box setting to iteratively update the coordinate perturbations based on gradients, or utilize the output model logits to estimate noisy gradients in the black-box setting. However, these attack methods are hard to be deployed in real-world scenarios since realistic 3D applications will not share any model details to users. Therefore, we explore a more challenging yet practical 3D attack setting, i.e., attacking point clouds with black-box hard labels, in which the attacker can only have access to the prediction label of the input. To tackle this setting, we propose a novel 3D attack method, termed 3D Hard-label attacker (3DHacker), based on the developed decision boundary algorithm to generate adversarial samples solely with the knowledge of class labels. Specifically, to construct the class-aware model decision boundary, 3DHacker first randomly fuses two point clouds of different classes in the spectral domain to craft their intermediate sample with high imperceptibility, then projects it onto the decision boundary via binary search. To restrict the final perturbation size, 3DHacker further introduces an iterative optimization strategy to move the intermediate sample along the decision boundary for generating adversarial point clouds with smallest trivial perturbations. Extensive evaluations show that, even in the challenging hard-label setting, 3DHacker still competitively outperforms existing 3D attacks regarding the attack performance as well as adversary quality.

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A Novel Relocating Recognition Algorithm in Overlapping Region for Multi-UAVs Cluster

A Novel Relocating Recognition Algorithm in Overlapping Regi...

引用

2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Li, Chen Qi, Xuelei Zhao, Juntao Liao, Xuanhong Lin, Zitian Ma, Hongjun Liang, Xinkai College of Information Science and Engineering Northeastern University Shenyang China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong High School Attached to Northeast Normal University International Curriculum Center Changchun China School of Automation Science and Engineering South China University of Technology Guangzhou China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

How improve the mapping efficiency and location accuracy of the multi-UAV cluster based on the distributed SLAM technology is a significant problem in overlapping regions. Therefore, this paper mainly proposes a novel relocation recognition algorithm for the multi-UAV cluster. First, the autonomous localization algorithm based on visual-inertial fusion is proposed. Then, the algorithm for vocabulary updating and relocating is designed. Finally, practical testing is implemented to verify and improve the efficiency of exploration ability and the accuracy of the autonomous navigation and positioning in the complex overlapping regions. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Few-Shot Camouflaged Object Segmentation

Few-Shot Camouflaged Object Segmentation

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International Joint Conference on Neural Networks (IJCNN)

作者： Ziqiu Wang Yuying Li Yang Yang Yamin Li Gaoyang Liu Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the domain of computer vision, Camouflaged Object Segmentation (COS) is a crucial task aimed at identifying objects that blend into their surroundings, with applications spanning diverse sectors such as military, medical, and beyond. Traditional COS techniques, which primarily depend on supervised learning, necessitate large-scale labeled datasets. However, the acquisition of sufficient camouflage images for such purposes is often constrained due to their scarcity and the high cost of manual annotation. Additionally, these conventional methods frequently struggle to generalize to novel, unseen classes. In response to these challenges, this paper proposes the Camouflage Few-Shot (CAMFS) framework, an innovative approach integrating few-shot learning into COS. The CAMFS framework comprises two main components: the Camouflaged-Meta module, which converts the semantic information of camouflaged objects into compact feature vectors to facilitate knowledge transfer from support to query images; and the Camouflaged-Base module, focused on refining edge detection and enhancing the contrast between foreground and background elements. To overcome the limitations of existing COS datasets, which are primarily designed for supervised learning, we have developed the COS-FSS dataset, the first public few-shot COS dataset. It is based on the COD10K dataset and supplemented with approximately 3200 additional camouflage images. We conducted extensive evaluations of our CAMFS framework on the COS-FSS dataset. Compared to existing COS models, CAMFS demonstrates an average improvement of 5.7% in S α and 14.02% in $F_\beta ^w$, while against few-shot segmentation models, it achieves a 5.97% increase in m-IoU. The dataset and additional resources are available at https://***/CAM-FSS/FSS-COD.

关键词： Image segmentation Military computing Supervised learning Semantics Refining Neural networks Object segmentation

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Physically Based Bidirectional Path Tacing Algorithm on the Tianhe-2 Supercomputer

Physically Based Bidirectional Path Tacing Algorithm on the ...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Yijia Wu Congpin Zhang Zhengwei Xu Ligang Li National Space Science Center Key Laboratory of Electronics and Information Technology for Space System Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences School of Computer & Information Engineering Henan Normal University Xinxiang Henan China

Physically based ray tracing is a technique that involves creating a photo-realistic image from a 3D scene description by utilizing the laws of physics to simulate the behavior of light and matter. However, due to the random length of light samples and irregular memory access patterns, it can suffer from low scalability and severe load imbalance. As a result, the development of a framework for multi-node parallelism algorithm in physically based ray tracing is a significant challenge. To tackle these issues, this paper introduces a novel algorithm named distributed parallel physically based bidirectional path tacing algorithm (DP-BDPT). Through analyzing the physical essence of bidirectional path tracing algorithm and the light transport equation, we have devised a framework that carefully divides tasks between inter-compute nodes and intra-compute nodes. To support high scalability, we have developed a physically based architecture. Additionally, we have proposed a hybrid scheduling method to achieve load balancing. To further enhance performance, we have analyzed several key factors that affect rendering performance and implemented adaptive optimization techniques. The experiments conducted on the Tianhe-2 Supercomputer involving typical and challenging 3D scenes, namely the Cornell-box, Veach-bidir, and Teapot-full experiments, have illustrated that DP-BDPT demonstrate near-linear speedups as the number of CPU cores scales from 1200 to 9600, thereby demonstrating their efficiency when operating on a ten-thousand-core system.

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SFDE-net: A Spatial-Frequency Domain Feature Enhancement Network for Cloud Detection

SFDE-net: A Spatial-Frequency Domain Feature Enhancement Net...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Baotong Su Siyan Li Wenguang Zheng Yao Chen The School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloud detection is a crucial step in the preprocessing of satellite remote sensing images. Existing methods tend to have misjudgments when dealing with specific scenarios, such as challenges in distinguishing thin clouds from the background and addressing missing cloud boundaries. To solve this problem, we designed a novel Spatial–Frequency Domain Feature Enhancement Block (SFDE) embedded in a U-shaped network called SFDE-net. SFDE consists of three units: the Dual Frequency Feature Unit (DFF), the Spatial Domain Feature Unit (SDF), and the Cross-Domain Feature Fusion Unit (CDF). DFF globally learns the boundaries and overall structure of clouds in the frequency domain, SDF captures fine-grained information in the spatial domain, and CDF adaptively fuses features from both DFF and SDF. Our method’s effectiveness was evaluated on two public datasets, GF-1 WFV and LandSat8. Extensive experiments demonstrated that the proposed SFDE-net achieved accurate detection accuracy and outperformed several state-of-the-art methods.

关键词： Earth Accuracy Satellites Artificial satellites Fuses Frequency-domain analysis Merging

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