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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Liu, Kuayue Gong, Fang Cui, Jinhao Zhang, Zhiqiang School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

In real data, there are dependencies between at-tributes, and most distance metrics make attribute independence assumptions, and although they show superior performance, when there are strong dependencies between attributes, it causes a certain degree of degradation to the distance metric performance. For this reason, this paper proposes a nominal attribute distance metric based on attribute dependencies. We conduct an extensive evaluation on a large number of UCI datasets and find that the method significantly outperforms currently available structural extension schemes in terms of negative log-likelihood and root-relative-square, while ensuring the robustness of the algorithm. © 2024 IEEE.

关键词： attribute independence assumption attributes dependence distance measure spec-tral clustering symmetry uncertainty

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The Method for Detecting Electrode Endpoints of Lithium-ion Batteries Based on Deep Learning 5

The Method for Detecting Electrode Endpoints of Lithium-ion ...

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5th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2024

作者： Li, Chi Chen, Deng School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331518677

In the industrial production of high-energy-density lithium batteries, accurately detecting electrode endpoint positions is crucial for maintaining appropriate electrode spacing, as improper spacing can lead to short circuits or thermal runaway, causing significant safety issues. Due to the vague characteristics of electrode endpoints, traditional detection methods face challenges in achieving high precision. This paper proposes a deep learning-based method for electrode endpoint detection called the Lithium Electrode Detection Network (LEDNet). LEDNet utilizes the CSPDarkNet53 network as the backbone for feature extraction and employs a Path Aggregation Network (PAN) in the Neck layer for multi-scale feature fusion, effectively enhancing the perception of both local details and global information of electrode endpoints. Additionally, a novel loss function named PedLoss is designed to refine error adjustments for fuzzy electrode endpoints, thereby improving the detection capabilities for ambiguous endpoints. Experiments conducted on a self-constructed dataset demonstrate that the proposed method achieves an AP50 of 96% for the electrode endpoint detection task, significantly enhancing the accuracy of endpoint detection and thereby improving the safety and reliability of the lithium-ion battery production process. © 2024 IEEE.

关键词： Lithium-ion batteries

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FCMDNet:A Feature Correlation-Based Multi-Scale Dual-Branch Network for Infrared and Visible Image Fusion 6

FCMDNet:A Feature Correlation-Based Multi-Scale Dual-Branch ...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Jiang, Zheng Chen, Xiangyang Zan, Guo Yang, Mingyu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

In recent years, deep learning techniques have been widely used in the field of infrared and visible image fusion (IVIF) due to their powerful feature representation. Existing methods usually use convolutional neural network (CNN), Transformer or Transformer-CNN to process different modal images. However, if the relationship between modality-specific and modality-shared features is ignored in the fusion network, the fused image can result in disturbed or missing modality information. To solve this problem, we propose a multi-scale dual-branch fusion network based on Transformer and CNN, called FCMDNet. Firstly, FCMDNet network introduces Multi-scale Patch Embedding based on Restormer module to extract multi-scale shallow features across modalities. Then, we design a dual-branch Transformer-CNN feature extractor, where the Transformer branch exploits the long and short-range attention of the Lite Transformer for low-frequency global features, the CNN branch uses residual dense connections to extract high-frequency local features. In addition, we propose a loss function based on feature correlation that exploits the correlation of low-frequency features and the uncorrelation of high-frequency features between different modalities to improve the fusion quality. Finally, an addition fusion strategy and two fusion layers are used to fuse the global and local features to obtain the final fused image. Experimental results on two public datasets show that our proposed FCMDNet has good performance on all of them. © 2024 IEEE.

关键词： Convolutional neural networks

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A Real-Time Video Detection Algorithm for UAV Based on YOLOv8 7

A Real-Time Video Detection Algorithm for UAV Based on YOLOv...

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7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Nie, Xin Wu, Qili School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350350760

Aiming at the common problem of small target detection in UAV real-time video surveillance, this paper proposes a UAV video detection algorithm based on improved YOLOv8 framework. By embedding a feature enhancement module specifically for small targets in the original YOLOv8 architecture, the algorithm effectively improves the recognition degree of the network for small targets. In order to avoid the excessive expansion of network parameters, the header network is simplified, the convolutional layer is removed and the number of channels is reduced to maintain the computational efficiency of the algorithm. Then this paper introduces the Channel Space Attention Mechanism module (CSAM), which makes the network pay more attention to the small target-dense region on the feature map by optimizing the weight distribution, thus enhancing the pertinence of target detection. In addition, a location-based label assignment strategy (LB-SimOTA) is proposed, which assigns different weights according to the intersection ratio (IoU) between the prediction frame and the real frame to optimize the overall quality of the network prediction frame. Experimental results on the VisDrone2019 dataset containing many small targets show that compared with the benchmark model YOLOv8-S, the proposed algorithm improves the accuracy of vehicle and pedestrian detection tasks by 8.44% and 8.12%, respectively, and achieves a frame rate of up to 90 FPS while maintaining the detection speed. ©2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research on Lightweight Steel Rebar End Surface Detection Algorithm Based on Improved YOLOv8 3

Research on Lightweight Steel Rebar End Surface Detection Al...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Zheng, Qiming Zeng, Xiang Jin Cai, Junyu Ma, Hetao School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

This paper proposes a steel bar detection algorithm model based on the YOLOv8 object detection algorithm. It improves the network structure by using EMA (Exponential Moving Average) attention to replace the original C2f module, and introduces the Dyhead detection head to achieve the unification of object detection head and attention, thereby enhancing the feature extraction capability of the base model. Experiments show that the improved method improves the mAP by 1.01 % compared with the original algorithm model, which can effectively improve the accuracy and speed of steel bar quantity detection, and is more in line with the needs of industrial automation. © 2024 IEEE.

关键词： Smart manufacturing

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Research on Domain Text Classification Algorithm Based on Multi-Granularity Fusion 24

Research on Domain Text Classification Algorithm Based on Mu...

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4th International Joint Conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Yao, Xiaotong Wu, Yuntao Yu, Xiaopeng Huang, Yufei School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China School of Management Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798400710100

The current research on domain text classification is primarily based on word granularity to facilitate the extraction and representation of text information. However, this approach is susceptible to the influence of out-of-vocabulary words and is insufficient for inter-word and inter-sentence semantic representation. Given the capacity of sentence template to effectively summarize sentence semantics, this paper proposes a domain text classification algorithm based on multi-granularity fusion. The algorithm introduces sentence template information to enhance text features from a semantic perspective, constructs embedding vectors of characters, as well as word and sentence templates. It extracts the contextual semantic features of these vectors using BiLSTM, further identifies key features of the characters and words through the attention mechanism, and finally, the information at each granularity is fused using the weighted fusion module to obtain the final semantic feature vectors. These vectors are then input into the softmax function for classification prediction. Experimental results demonstrate that the sentence template information significantly improves the model's ability to represent text semantics. Additionally, the weighted feature fusion method effectively retains and utilizes more feature information. These two improvements enhance classification accuracy by 1.77% and 1.49%, respectively. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Information fusion

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The Method for Detecting Abnormal Actions in Industrial Robots Based on Robots Spatio-Temporal Graph Normalizing Flow 5

The Method for Detecting Abnormal Actions in Industrial Robo...

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5th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2024

作者： Xiao, Wen Chen, Deng School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331518677

Rapid detection and response to abnormal movements of industrial robots can effectively reduce the risk of injury. Abnormal action detection requires modeling and distinguishing the complex and variable action patterns of industrial robots, which needs to take into account the spatio-temporal coupling and dynamic characteristics of industrial robots' actions, while efficient processing of spatio-temporal features and complex dynamics models requires a large amount of computational resources, which limits the accuracy and response speed of detection. To this end, we propose a robots spatio-temporal graph normalized flow (RSTG-NF) model for abnormal action detection of industrial robots. The model uses a Robots Spatio-Temporal Convolutional Network (RSTGC) to capture and differentiate the complex action relationships and motion patterns of industrial robots actions in both temporal and spatial dimensions. The frame-level action sequences are placed in a bi-directional mapping between the high-dimensional data space and the low-dimensional potential space through normalized flow, and the anomaly scores are evaluated based on the mapped data distributions for localization and detection of anomalies. Extensive experiments on a home-made six-axis industrial robots action dataset (IRADs) show an AUC score of 92.18% for frame-level detection and an inference speed of 79 FPS. © 2024 IEEE.

关键词： Industrial robots

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DocTrKan: Document Image Enhancement Based on KAN-Enhanced Transformer Networks 3

DocTrKan: Document Image Enhancement Based on KAN-Enhanced T...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Zheng, Youkang Liu, Jun Zhou, Sai School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

Document images serve as a medium for transmitting information;however, they are influenced by numerous environmental factors during capture, complicating the identification and processing of their content. In the digital era, it is crucial to eliminate noise from document images to enhance readability. To address this challenge, we propose a novel transformer-based document image enhancement algorithm utilizing KAN enhancement, designed to improve both machine-printed and handwritten document images in an end-to-end manner. The encoder operates directly on pixel blocks with positional information, bypassing traditional convolutional layers for feature extraction. A KAN network is incorporated into the encoder to enhance the model's expressive power, reduce parameter count, and improve optimization efficiency. A decoder is employed to reconstruct the clean image. In testing on the DIBCO dataset, DocTrKan surpasses traditional transformer and threshold-based methods in enhancement performance. © 2024 IEEE.

关键词： Electric transformer testing

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Mean Field Analysis of Serverless Edge Computing for Smart Vehicle Applications 4

Mean Field Analysis of Serverless Edge Computing for Smart V...

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4th International Conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Yao, Yuan He, Rong Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331534622

Smart cities and vehicles generate numerous time sensitive tasks, making edge computing a key solution for reducing latency. However, using serverless models at the edge faces challenges like dynamic city traffic and resource constraints. This study analyzes serverless edge computing for smart vehicles through modeling and experiments. we develop a mean field model captures steady-state system behavior under stochastic job arrivals, enabling efficient analysis of metrics like mean sojourn time and service degradation probability. Through experiments and simulations, we demonstrate the feasibility of serverless edge servers for low-latency offloading, providing insights for optimizing these systems in urban environments. © 2024 IEEE.

关键词： Edge Computing Mean field Model Performance Analysis Serverless

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Research on facial expression recognition based on multimodal data fusion and neural network

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 47-55页

作者： Han, Yi Wang, Xubin Lu, Zhengyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Anyang Institute of Technology Department of Computer Science and Information Engineering Henan Province Anyang China School of Computer Science and Information Engineering Shanghai Institute of Technology FengXian District Shanghai China

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data fusion

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