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SSD object detection algorithm based on knowledge map

International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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CC-Net: Consistency Learning Combined with Contrastive Learning for Kidney Ultrasound Segmentation

CC-Net: Consistency Learning Combined with Contrastive Learn...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Luo, Yang Liu, Jun Ding, Mengqian 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 China

ISBN: (纸本)9781665410205

Due to the scarcity of medical images with labels and the fact that medical image data is not easily accessible. Kidney ultrasound (KUS) images have speckle noise, low image quality, acoustic shadows, and other reasons, which pose significant challenges to the kidney ultrasound image segmentation task. To solve the above two problems, a novel consistency learning and contrastive learning-based kidney ultrasound image segmentation method (CC-Net) has been proposed. Specifically, the student-teacher model was adopted as the semi-supervised learning framework and uncertainty estimation was introduced. The Consensus Uncertainty Map (CUM) strategy was proposed to generate segmentation results for the teacher model when calculating uncertainty. To solve the problems of acoustic shadows and low image quality in kidney ultrasound images, a kidney ultrasound image data augmentation method (KUS-AUG) and a convolutional block (KUS-CONV) tailored to the characteristics of kidney ultrasound images were proposed. In addition, contrastive learning was implemented to enhance the encoder's ability to learn more semantic information, due to its inherent advantage in learning data distribution. Finally, a series of experiments were conducted on the ultrasound image dataset and the method achieved the best performance. © 2024 IEEE.

关键词： Contrastive Learning

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A Lightweight and key Escrow-Free Access Control Scheme on Encrypted Data in Edge Computing 12

A Lightweight and Key Escrow-Free Access Control Scheme on E...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： He, Heng Chen, Yuejia Lu, Jianfeng Zheng, Jiacheng 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 China

ISBN: (纸本)9798331511074

Attribute-based encryption provides fine-grained access control, ensuring data security in edge computing. However, existing access control schemes face issues such as high computational complexity due to bilinear pairing and key escrow with a single authority, which limit their effectiveness for lightweight terminal devices. To address these challenges, we propose a lightweight and key escrow-free access control scheme on encrypted data. This scheme replaces bilinear pairing with fast elliptic curve scalar multiplication, transferring most computational tasks to edge servers while preserving privacy, thus alleviating the computational burden on terminal devices. Additionally, we design a blockchain-based distributed key management method that allows multiple edge servers to distribute private keys collaboratively, resolving the key-escrow issue. Security analysis and performance evaluation demonstrate that our scheme effectively ensures data confidentiality, resists collusion attacks, and achieves high computational efficiency on the user side with low storage overheads, making it highly suitable for lightweight devices in edge computing. © 2024 IEEE.

关键词： Ciphertext

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Cross-video Identity Correlating for Person Re-identification Pre-training 38

Cross-video Identity Correlating for Person Re-identificatio...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zuo, Jialong Nie, Ying Zhou, Hanyu Zhang, Huaxin Wang, Haoyu Guo, Tianyu Sang, Nong Gao, Changxin National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Huawei Noah's Ark Lab Canada

Recent researches have proven that pre-training on large-scale person images extracted from internet videos is an effective way in learning better representations for person re-identification. However, these researches are mostly confined to pre-training at the instance-level or single-video tracklet-level. They ignore the identity-invariance in images of the same person across different videos, which is a key focus in person re-identification. To address this issue, we propose a Cross-video Identity-cOrrelating pre-traiNing (CION) framework. Defining a noise concept that comprehensively considers both intra-identity consistency and inter-identity discrimination, CION seeks the identity correlation from cross-video images by modeling it as a progressive multi-level denoising problem. Furthermore, an identity-guided self-distillation loss is proposed to implement better large-scale pre-training by mining the identity-invariance within person images. We conduct extensive experiments to verify the superiority of our CION in terms of efficiency and performance. CION achieves significantly leading performance with even fewer training samples. For example, compared with the previous state-of-the-art [9], CION with the same ResNet50-IBN achieves higher mAP of 93.3% and 74.3% on Market1501 and MSMT17, while only utilizing 8% training samples. Finally, with CION demonstrating superior model-agnostic ability, we contribute a model zoo named ReIDZoo to meet diverse research and application needs in this field. It contains a series of CION pre-trained models with spanning structures and parameters, totaling 32 models with 10 different structures, including GhostNet, ConvNext, RepViT, FastViT and so on. © 2024 Neural information processing systems foundation. All rights reserved.

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ORB image feature extraction algorithm based on fuzzy control 3

ORB image feature extraction algorithm based on fuzzy contro...

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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Li, Ruilong College 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 China

ISBN: (纸本)9781510681880

With the increasing demands for real-time accuracy in visual SLAM (Simultaneous Localization and Mapping), the critical front-end component of SLAM systems has become the extraction and matching of image features to estimate camera motion pose. Existing feature extraction methods are susceptible to local feature point density and even overlap due to uncertainties in lighting intensity and surrounding environments, leading to uneven distribution of overall feature points. This, in turn, affects the accuracy of feature matching, thereby impacting the performance of the SLAM process. To address these challenges, this paper proposes a novel ORB image feature extraction algorithm based on fuzzy control. Initially, a Gaussian pyramid of the image is constructed, and the Intensity Centroid is calculated to ensure scale and rotation invariance. Subsequently, candidate feature points are extracted using ORB at each pyramid level, then the candidate feature points are selectively filtered through fuzzy control based on the number of candidate feature points and the magnitude of their response values. Finally, a BRIEF descriptor is established for feature matching. The proposed Fuzzy-Controlled ORB Feature Extraction (OFFC) algorithm was experimentally validated on open-source TUM and Mikolajczyk datasets. Results indicate that OFFC successfully avoids excessive density and overlap of extracted feature points, reduces mismatches in image feature points, and enhances the accuracy of feature matching. Notably, OFFC demonstrates adaptability and maintains robust performance in three challenging scenarios: image blurring, varying brightness, and dense similarity of image blocks, highlighting its significant practical value for visual SLAM. © 2024 SPIE.

关键词： Fuzzy control

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The Dual Path Multi-Feature Fusion Model combining CNN and Transformer for Age Estimation of Caenorhabditis elegans

The Dual Path Multi-Feature Fusion Model combining CNN and T...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ding, Mengqian Liu, Jun Luo, Yang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350386226

The relatively simple genome, short life cycle, and easy observation of Caenorhabditis elegans (C. elegans) make it an excellent model organism for aging research. Age-stage prediction of C. elegans can help to assess the effectiveness of anti-aging interventions, better explore the role of individual differences in the aging process, and thus promote the development of medicine. However, there are still some problems in accurately predicting the age stages of C. elegans. On the one hand, there may be individual differences among C. elegans due to natural variation and environmental factors. On the other hand, nematode data sets are scarce and image information is complex. In this paper, we perform the age prediction task by studying the body contour morphology, biomarkers, and internal organ changes of C. elegans. Specifically, to address the effects of individual differences and datasets, this paper combines the body contour features, protein aggregation features, and pharyngeal terminal bulb features of C. elegans. In addition, considering the complex information of C. elegans images, this paper proposes a hybrid CNN and Transformer feature extraction method (DFCT), which can fully extract the global and local features of the image. A Dual Path Adaptive Feature Fusion Module (DAFM) is used to fuse the features. A series of experiments are conducted to verify the effectiveness of the proposed method. The experimental results show that the method proposed in this paper outperforms other methods in terms of accuracy and precision, especially in dividing 25 age stages into 3 categories, with an accuracy of 85.93%. © 2024 IEEE.

关键词： age estimation biological image analysis Caenorhabditis elegans CNN and Transformer image classification

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RIS-Aided Cell-Free Massive MIMO Systems With Low-Resolution ADCs: Uplink Performance Analysis and Optimization

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IEEE Internet of Things Journal 2025年第11期12卷 18249-18265页

作者： Xiong, Youzhi Tang, Liu Sun, Sanshan Yang, Songjie Liu, Li Mao, Sun Zhang, Zhongpei Wei, Ning Sichuan Normal University Laboratory of Information Intelligent Processing and Communications College of Physics and Electronic Engineering Chengdu610101 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Sichuan Normal University College of Computer Science Chengdu610101 China

This article investigates the uplink performance of reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems over spatially correlated Rayleigh fading channels. We consider multiple RISs and low-resolution analog-to-digital converters (ADCs) to improve the system energy efficiency (EE). We first provide an aggregated channel estimation technique with less pilot overhead. By exploiting the statistical channel state information (CSI), we further optimize the RISs’ phase shifts with the goal of minimizing the total normalized mean square error (NMSE) of the estimated aggregated channels. Subsequently, we derive the closed-form expression of the uplink spectral efficiency (SE) for quantization-aware minimum mean-square error (MMSE) combining. Third, based on the closed-form SE expression and power consumption model, we formulate and solve an optimization problem that maximizes the uplink EE under the constraints of transmit power and total ADC quantization bits. Specifically, by leveraging the Dinkelbach transform, Lagrangian dual transform, and fractional programming (FP) techniques, an alternating optimization (AO)-based algorithm is proposed to jointly obtain the bit allocation (BA) scheme among all access points (APs) and the uplink power control (PC) strategy for all users. Finally, numerical results validate the correctness of the closed-form SE expression and show the effectiveness of the proposed optimization methods for phase shift design and EE maximization. © 2014 IEEE.

关键词： Mean square error

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Discrete Whale Optimization Algorithm for Capacitated Vehicle Routing Problem

Discrete Whale Optimization Algorithm for Capacitated Vehicl...

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2023 China Automation Congress, CAC 2023

作者： Chen, Huiyu Sun, Lijun Chen, Tianfei College of Information Science and Engineering Henan University of Technology Henan China Henan University of Technology Zhengzhou Key Laboratory of Machine Perception and Intelligent System Henan China

ISBN: (纸本)9798350303759

Capacitated vehicle routing problem (CVRP) is a classical combinatorial optimization problem, which plays an important role in the logistics field and belongs to the NP-hard problem. Discrete whale optimization algorithm (DWOA) with a routing first and grouping later solution generating technique is proposed to handle the CVRP. The algorithm redefines the classical whale optimization algorithm's encircling prey method, bubble-net attacking method, and exploration phase method. Through the improved whale optimization algorithm, the optimal planning of logistics distribution vehicle routing is realized. Finally, an experimental demonstration of the DWOA algorithm's success offers a novel approach for resolving CVRP. © 2023 IEEE.

关键词： Vehicle routing

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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

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Tsinghua Science and technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite System(BDS) long short-term memory(LSTM) multipath classification

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Transmission line inspection system based on high performance unmanned helicopters 4

Transmission line inspection system based on high performanc...

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4th International Conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2024

作者： Li, Wei Li, Ming Meng, Xiangjiao Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing Tianjin Zhong Wei Aerospace Data System Technology Co. Ltd. Tianjin China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

A high-performance unmanned helicopter based transmission line inspection system is proposed to address the issues of low single operation efficiency, low degree of autonomy, and single operation mode in the current multi rotor unmanned aerial vehicle inspection system. This system is based on an unmanned helicopter platform, with customized research and development of load quick replacement and mounting structural components, airborne load control units, optoelectronic pods, autonomous inspection software systems, and other parts. The number of towers that can be inspected in a single fine inspection operation is not less than 20 (visibility, distance between towers is 0.3 km), and the photographic data can meet the requirements of identifying pin level targets (10mm) 50 meters away. Through flight verification testing, it has been shown that the various working modes of the system are operating normally, and autonomous inspections do not require human intervention, greatly improving the efficiency of transmission line inspections. © 2024 SPIE.

关键词： Electric lines

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