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International Conference on Computer Supported Cooperative Work in Design

作者： Yuechen Zhang Aimin Li Xiaotong Kong Wenqiang Li Zhiyao Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Steel surface defect detection is very important in industrial quality inspection, and there have been many studies on Steel surface defect detection in recent years. Existing steel surface defect detection algorithms suffer from low detection accuracy and high model complexity. To improve the above problems, this paper proposes an optimized target detection algorithm based on YOLOv5. Firstly, the original spatial pyramid pooling (SPP) module is replaced by the SPPFCSPC module to better capture the target and scene information at different scales, and to improve the sensory field and feature expression ability of the model. Secondly, we propose the C2F-Faster module based on FasterNet and C2F module to replace the C3 module of the backbone network, which perfectly integrates the ideas of PConv and ELAN and ensures the detection accuracy while effectively reducing the model size. Finally, the Dynamic Head detection head is fused in the head of the model to improve the detection capability of the target detection head using the separated attention mechanism. We conducted experimental validation on the widely used NEU-DET dataset. The experimental results show that the improved model improves the original YOLOv5 model over the original YOLOv5 model by 3.6% in mAP @50 , 9.9% and 2.8% in AP and AR, respectively, and the amount of model parameters is reduced by 13.2%. The improved model also outperforms SSD, FasterRCNN, YOLOv5, YOLOv6, YOLOv7, YOLOv8 and other mainstream target detection models, which better meets the requirements of actual industrial production on the accuracy and speed of steel surface defects detection model.

关键词： YOLO Accuracy Attention mechanisms Computational modeling Heuristic algorithms Inspection Data models

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RTS-DETR: Efficient Real-Time DETR for Small Object Detection

RTS-DETR: Efficient Real-Time DETR for Small Object Detectio...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wenqiang Li Aimin Li Zhiyao Li Xiaotong Kong Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, object detection models DEtection TRansformer (DETR) series based on Transformer architecture have played a huge role in various fields. However, the DETR series models are not satisfactory in small object detection. Mainly due to the huge amount of calculation of DETR, a lot of feature information will be lost in the feature fusion stage and the low tolerance of small objects to Intersection over Union (IoU). In order to solve the above problems, we propose a near real-time detection model RTS-DETR. In this paper, we revisit Real-Time DEtection TRansformer (RT-DETR), which effectively handles multi-scale features by decoupling intra-scale interaction and cross-scale fusion, but this will lose a lot of positive local information. To this end, we have improved the efficient hybrid encoder. We propose a new positional encoding method that enables the hybrid encoder to more accurately convert the input feature sequence into a high-dimensional representation, and propose a new feature fusion module to enhance the model's ability to capture local features. Furthermore, in order to improve the tolerance of small objects to IoU, we combine Normalized Wasserstein Distance (NWD) with Shape-IoU for the optimization model. This method more accurately takes into account the shape and size of objects, thereby improving detection accuracy. Our model achieves an accuracy of 38.8% (in terms of mAP @0.5 ) on the widely used VisDrone dataset, which improves the accuracy by 2.5% compared to RT-DETR with ResNet-18 as the backbone network.

关键词： Accuracy Shape Fuses Optimization models Semantics Object detection Feature extraction Transformers Real-time systems Encoding

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RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Prohibited Items

RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Proh...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaotong Kong Aimin Li Wenqiang Li Zhiyao Li Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

X-ray image detection is essential for ensuring public safety, but traditional methods rely heavily on human analysis and are relatively inefficient. To address this issue, this paper proposes a new object detection model called RFSD-YOLO, which is based on the YOLOv8 model and is specifically designed for detecting prohibited items in X-ray images. The model adopts the RFCAConv structure instead of the conventional convolutional operation. This allows for independent parameterisation among the convolutional kernels, enhancing the model's ability to capture and express potential prohibited item features. The neck section of the model includes the GSConv and VoVgscspdesigns, which aim to balance complexity and parameter size while maintaining detection performance and reducing computational burden. Additionally, we have introduced a dynamic head structure, DyHead, which replaces the traditional detection head design and improves detection accuracy without adding computational cost. The experimental results demonstrate that our enhanced model surpasses the current state-of-the-art object detection models in detecting prohibited items. Additionally, we introduce a simplified version of the RFSD-YOLOnano model to cater to resource-constrained environments. This streamlined model improves AP, AR, and mAP by 4.4%, 14.6%, and 3.4%, respectively, compared to YOLOv8n. This series of innovations not only validates the superiority of our model, but also provides new solutions in the field of automated detection of prohibited items for X-ray security screening.

关键词： Technological innovation Head Accuracy Computational modeling Object detection Data models Public security Complexity theory security X-ray imaging

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HSD-YOLO: A Lightweight and Accurate Method for PCB Defect Detection

HSD-YOLO: A Lightweight and Accurate Method for PCB Defect D...

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International Joint Conference on Neural networks (IJCNN)

作者： Zhiyao Li Aimin Li Wenqiang Li Xiaotong Kong Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

PCB defect detection is a typical small objects detection task, as with other objects, there is a small object size, the detection process is susceptible to the problem of background interference. In practical industrial production, it is difficult for existing object detection models to realize the balance between accuracy and real-time performance. Therefore, we propose a new and lightweight object detection model. And we named it Hsd-YOLO. Firstly, HGNetv2, the backbone of the new paradigm RT-DETR for object detection, is chosen as the backbone of our model, which makes the model more lightweight and reduces the number of parameters and computation while guaranteeing the accuracy of the model’s detection. Secondly, we use the more lightweight convolutional GSConv, which is introduced into the neck to make the model balance between accuracy and speed. Finally, a unified dynamic head framework, DyHead (Dynamic Head), is introduced to make the model improve the representation of the object detection head without increasing the computational overhead. We perform comparison experiments as well as ablation experiments on a publicly available PCB defect datasets to fully illustrate the effectiveness of ours model. We conduct ablation experiments on the current state-of-the-art single-stage model YOLOv8, and our model improves AP, AR, mAP@0.5 and mAP@0.5-0.9 by 3.8%, 0.2%, 0.6% and 2.2%, respectively, and reduces the number of parameters in 385312 while ensuring accuracy. Comparing with major object detection models, our model performs the best in accuracy.

关键词： Accuracy Computational modeling Neural networks Object detection Quality control Production Real-time systems

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

arXiv

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arXiv 2024年

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2024, CC BY-NC-SA.

关键词： Speech recognition

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LDD-YOLO: An Improved Lightweight Detection Method for Steel Surface Defects Based on YOLOv8

LDD-YOLO: An Improved Lightweight Detection Method for Steel...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yuechen Zhang Aimin Li Zhiyao Li Xiaotong Kong Wenqiang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Steel is an indispensable raw material in the industrial field, steel surface defects seriously affect the quality of steel, in recent years a lot of research has been carried out on the detection of steel surface defects. Existing steel defect detection methods are unable to fully mine the underlying feature information of the target image and do not achieve a dynamic balance between accuracy and speed. To address the above problems, this paper proposes an optimised target detection algorithm based on YOLOv8. First, we proposed the DMCA module, which combines the ideas of deformable convolution and multi-channel self-attention mechanism. We developed a strengthen self-attention module to enhance the process of deformable convolutional generation of offsets, so that the model can better adapt to the complex shapes of different defective targets and extract features at a deeper level. Secondly, using the idea of LKA (Large Kernel Attention), we propose the LF-MSPP lightweight module with long-range dependence and adaptive capability to capture the tele-relationships with small computational cost and parameters, improved the problem of missing defective feature information. Finally, we replaced the head of the original YOLOv8 with a Dynamic Head and used the split attention mechanism to improve the head detection capabilities while ensuring lightweight. We conduct extensive experiments on the widely used Northeastern University steel defect dataset NEU-DET. Experimental results show that the improved model improves mAP@50, mAP@50-95, AP and AR indicators by 2.4%, 2.0%, 6.1%, and 3.4% respectively compared with the original YOLOv8 model, and the number of model parameters is reduced by 11.2%. The improved model is also better than mainstream defect target detection models such as SSD, Retinanet, FasterRCNN, YOLOv5, YOLOv6, YOLOv7, YOLOv8, etc, and can better meet the accuracy and speed requirements of actual industrial production for steel surface defect detection mode

关键词： YOLO Accuracy Head Three-dimensional displays Shape Convolution Feature extraction Steel Kernel Defect detection

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FedDE: A Data Augmentation Strategy to Combat the Non-IID Issue in Federated Learning

FedDE: A Data Augmentation Strategy to Combat the Non-IID Is...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Guopeng Hao Liting Geng Feng Li Chunxiao Wang Zhigang Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

Federated Learning (FL) is a distributed machine learning framework that protects data privacy by allowing multiple end devices to train machine learning models collaboratively without sharing raw data. However, data on various end devices are typically Non-Independent and Identically Distributed (Non-IID), leading to difficulties in model convergence, client model drift, and reduced generalization ability. Moreover, highly heterogeneous Non-IID data require more communication rounds, resulting in high communication costs and significantly reduced model accuracy. We propose a FL framework(FedDE) based on Denoising Diffusion Probabilistic Models (DDPM) and Knowl-edge Distillation from the data and model fusion perspective to tackle these challenges. In the FedDE framework, the DDPM is used as a class balancer to alleviate the problem of uneven label distribution. Additionally, the server uses labeled data for ensemble distillation, flexibly aggregating local clients and reducing the number of communication rounds. We conducted extensive experiments on multiple public benchmark datasets such as MNIST, FashionMNIST, and CIFAR10. The test results demonstrate that our proposed method outperforms state-of-the-art methods in accuracy and significantly reduces the number of communication rounds.

关键词： Data privacy Accuracy Federated learning Distributed databases Diffusion models Data augmentation Data models Robustness Sensors Servers

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MF-YOLO: Multimodal Fusion for Remote Sensing Object Detection Based on YOLOv5s

MF-YOLO: Multimodal Fusion for Remote Sensing Object Detecti...

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International Conference on Computer Supported Cooperative Work in Design

作者： Wenqiang Li Aimin Li Xiaotong Kong Yuechen Zhang Zhiyao Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Remote sensing object detection has flourished at a rapid rate of development. In remote sensing images (RSI), most algorithms perform well in detecting medium and large objects but perform poorly on small objects. This is because small objects occupy fewer pixels in the entire image, and the detector is more likely to be biased towards detecting large objects. In addition, the huge background in the image also introduces noise, causing many detection failures. Most approaches solve the problem of small object detection by increasing the depth of the neural network, but the results are not satisfactory. To solve the above problems, we propose a new method, MF-YOLO, that utilizes fused infrared (IR) and red-green-blue (RGB) images for remote sensing object detection. Through multimodal fusion (MF), we can obtain more positive information, thereby improving detection accuracy. In addition, we introduced the Bi-Level Routing Attention (BRA) module to improve the YOLOv5s model structure, and proposed a new loss function to enhance the model’s learning discrimination ability. Experimental results show that the accuracy of MF-YOLO on the VEDAI and NWPU VHR-10 datasets is 76.62% and 91.63% respectively (in terms of mAP @0.5 ). Compared with the existing state-of-the-art, the detection accuracy is significantly improved. In addition, our model has fewer weight parameters than YOLOv5s, allowing it to achieve relatively high near-real-time detection speed.

关键词： YOLO Accuracy Federated learning Noise Neural networks Prediction algorithms Routing

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A Non-Intrusive Speech Quality Assessment Method for Low-Rate Communication

A Non-Intrusive Speech Quality Assessment Method for Low-Rat...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Lingxia Lin Ye Li Peng Zhang Xingye Yu Tianyu Cai Hongyuan Zou Min Zheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Muding Information Technology Co. Ltd China Electric Power Research Institute Co. Ltd Beifing China

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Although PESQ (Perceptual Evaluation of Speech Quality) serves as an important benchmark for assessing narrow-band speech quality, its effectiveness depends on the availability of reference signals, which is not always feasible in practical applications. To surmount this limitation, numerous non-intrusive assessment methods have been proposed. However, most of them focus on wideband speech, with scant research dedicated to narrowband speech. To this end, this paper proposes a non-intrusive speech quality assessment model suitable for low-bitrate narrowband communication scenarios. The model integrates Convolutional Neural networks (CNN), Bidirectional Long Short-Term Memory (BLSTM) networks, and a frequency band attention mechanism, effectively extracting key features from narrowband speech signals and conducting quality evaluation. Experimental results show that the proposed model significantly outperforms the baselines, achieving a Mean Squared Error (RMSE) of 0.0200, a Pearson Linear Correlation Coefficient (PLCC) of 0.955, and a Spearman Rank Correlation Coefficient (SRCC) of 0.954, demonstrating higher accuracy and robustness.

关键词： Correlation coefficient Accuracy Attention mechanisms Speech enhancement Feature extraction Robustness Quality assessment Convolutional neural networks Integrated circuit modeling Narrowband

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PWPH:Proactive Deepfake Detection Method Based on Watermarking and Perceptual Hashing

PWPH:Proactive Deepfake Detection Method Based on Watermarki...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jian Li Shuanshuan Li Bin Ma Chunpeng Wang Linna Zhou Fei Wang Yule Wang Miaomiao Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The popularity of Deepfake technology has raised the challenge of recognizing real and fake faces. While detection methods already exist, most of them are passive forensics and face challenges of generalizability and migration. Currently, some research attempts to protect the original image by priorly inserting invisible information. However, there are still shortcomings in terms of image quality and information robustness due to information embedding, i.e., watermarking. Therefore, we employ the robustness of perceptual hash coding and combine it with information hiding techniques to propose a proactive Deepfake detection solution, referred to as PWPH in this paper. Our approach is simple and efficient: first, the image containing a face is divided into two parts: FA (face area), and NFA (non-face area). A perceptual hash code is generated from the non-face area (NFA). Then, the hash codes are embedded as watermarks into the FA. At the extraction stage, we use the same method as the encoder to retrieve the embedded watermark from FA. The watermark is then compared with the hash code generated from the NFA of the detected image. The extracted watermark is sensitive to distortion and may vanish during Deepfake processing. Experimental results validate that our method, requiring just one encoder and decoder, enables active detection and source tracking. Furthermore, its efficacy in typical Deepfake scenarios such as face swapping and expression reconstruction is confirmed through comparison with prior arts.

关键词： Learning systems Image quality Deepfakes Codes Face recognition Watermarking Robustness Decoding Faces Image reconstruction

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