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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Chen, Hongwei Huang, Zhihua Lu, Kexin Liu, Hanzheng Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumchi China

ISBN: (纸本)9798350324303

Currently, the COVID-19 virus is in a low-level epidemic state but still spreading in society. To timely cut off the transmission chain, early screening for COVID-19 is crucial. However, adding extra network modules during training may lead to module stacking errors and increased complexity. In this paper, we propose an optimization method based on residual stage networks (ResStage) and polarized self-Attention mechanism to improve the detection ability of the model. This method provides better paths for information propagation across different layers of the network and extraction more refined and high-quality features. Results show that the fusion model based on ResStage and polarized self-Attention can improve the detection accuracy of COVID-19 without increasing excessive parameters, achieving an AUC score of 86.53%. Moreover, this paper also subdivides the training tasks, including COVID-19 positive patients, COVID-19 positive patients with coughing, healthy individuals with coughing, asthma patients with coughing, and healthy asymptomatic individuals. These different tasks help to evaluate the performance of model and provide more references for practical applications. © 2023 IEEE.

关键词： COVID-19

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MetricGAN+ Speech Enhancement Model Combining Multi-scale and Multi-resolution Feature Network and Joint Perception Loss 4

MetricGAN+ Speech Enhancement Model Combining Multi-scale an...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Guo, Chuangjian Huang, Zhihua Li, Hui Song, Jingyi Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumchi China

ISBN: (纸本)9798350324303

Recently, generative adversarial networks have been widely used in speech enhancement. However, some existing speech enhancement methods only aim to optimize a single perceptual metric, which may cause other perceptual metrics to be suboptimal. In this paper, we propose a joint perceptual loss function to guide the training of speech enhancement models. In addition, we use a multi-scale and multi-resolution feature extraction network in the discriminator of MetriGAN+model, so that the model can make full use of high-level context information and low-level time-frequency features, so as to improve the prediction performance of the discriminator and guide the training of the generator better. The evaluation results on the VoiceBank-DEMAND dataset show that the proposed model can effectively improve speech quality and intelligibility, and can improve the PESQ score by 0.19 compared to the previous MetricGAN+. © 2023 IEEE.

关键词： Speech enhancement

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A Lightweight UAV Visual Obstacle Avoidance Algorithm Based on Improved YOLOv8

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Computers, Materials & Continua 2024年第11期81卷 2607-2627页

作者： Zongdong Du Xuefeng Feng Feng Li Qinglong Xian Zhenhong Jia College of Computer Science and Technology Xinjiang UniversityUrumqi830046China Signal Detection and Processing Autonomous Region Key Laboratory Xinjiang UniversityUrumqi830046China Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing Urumqi830000China

The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and *** propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle *** algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)***,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection *** on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different *** particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance *** verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was *** experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.

关键词： Unmanned aerial vehicle obstacle detection obstacle avoidance algorithm

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Change detection based on optimized guided filtering in wide-field video image under low illumination

Change detection based on optimized guided filtering in wide...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Xie, Chengyou Jia, Zhenhong Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

To effectively and accurately detect regions with subtle changes in wide-field videos captured by eagle-eye equipment in low-light environments. A two-level change detection (CD) algorithm based on guided filter fusion is proposed to achieve better separation in complicated scenarios. First, the difference images (DIs) is obtained through the difference operator and the extreme minimum ratio operator. At the same time, using Principal Component Analysis (PCA) fuse the global brightness component and the V component are extracted from images to obtain the optimized brightness map. Then, the saliency map is obtained from the optimized brightness map based on the maximum salient pixel and spatial continuity characteristics as a guide for DIs fusion. Finally, a two-level method is proposed to combine the threshold method and clustering algorithm to further suppress the noise and DI normalization. Then, using the K-means algorithm to to generate the final binary change map. Experimental results show that the proposed algorithm can accurately and effectively detect changes in Eagle Eye surveillance video images in low-light environments. Compared with other algorithms, this algorithm reduces the running time and effectively minimizes the interference of light sources and dark spot noise in low-light environments. © 2024 SPIE.

关键词： Luminance

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Heavy snow removal in video based on tensor dimensionality reduction and simultaneous sparse coding

Heavy snow removal in video based on tensor dimensionality r...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Wei, Guofeng Jia, Zhenhong Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

We propose a novel snow removal algorithm. First, we make full use of the high-dimensional character- istics of video data and propose a tensor dimensionality reduction-based robust principal component analysis (TDR-RPCA) method to reconstruct clean background, which improves the traditional RPCA’s ability to handle four-dimensional data and solves the problems with artifacts residue. Then a penalty regularization term-based outlier detection method is used to detect moving objects and glob- al large snowflakes. A binary mask containing moving object and snowflakes is obtained. According to the mask, we design an adaptive thresholding strategy to remove snowflakes. Our scheme can effectively remove global snowflakes. Considering the sparse characteristic of snowflakes, tinny snowflakes in front of moving objects can be regarded as noise;hence, we deal with these tiny snowflakes using Gaussian-scale mixture-based simultaneous sparse coding. Experiments show that our method can remove snowflakes in videos captured during heavy snow conditions and eliminate artifact residue, which outperforms other state-of-the-art methods. © 2024 SPIE.

关键词： Tensors

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Digital image biological detection technology based on the porous silicon periodic crystals film

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Optoelectronics Letters 2021年第9期17卷 552-557页

作者： YANG Jianfeng JIA Zhenhong LüXiaoyi HUANG Xiaohui WANG Jiajia School of Physical Science and Technology Xinjiang UniversityUrumqi 830046China School of Information Science and Engineering Xinjiang UniversityUrumqi 830046China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous RegionXinjiang UniversityUrumqi 830046China

In order to solve this problem,a new biological detection method is proposed,which makes use of the characteristics of optical transmission at the edge of the spectral band gap and sensitive to refractive index *** the probe light with wavelength at the edge of the Bragg band gap of porous silicon is incident on the surface of porous silicon,the change of refractive index caused by deoxyribonucleic acid(DNA)reaction in porous silicon will affect the detection light intensity transmitted from the porous silicon *** analyzing the change of the average gray value of the transmitted light image,the concentration of the DNA can be obtained.

关键词： porous image refractive

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End-to-end Speech Enhancement Using Self-Attention Generative Adversarial Networks 6

End-to-end Speech Enhancement Using Self-Attention Generativ...

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6th International Symposium on Computer and Information processing Technology, ISCIPT 2021

作者： Cao, Zhonghui Huang, Zhihua Xinjaing University College of Information Science and Engineering Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumqi830001 China

ISBN: (纸本)9781665441377

Generative adversarial networks (GAN) achieve a good level of success in the speech enhancement (SE) task in recent years. However, traditional GAN for SE generates enhanced speech only as a function of local points, such that the model has difficulity in capturing long-range dependency and non-local information. In this paper, a model named self-attention GAN (ASEGAN) is proposed to estabilish connections between every sample and the rest samples in the time domain. Attention mechanism is used to model long-range dependency modeling for SE tasks. Armed with self-attention, the generator can generate the enhanced speech in which fine details at every sample are carefully coordinated with fine details in distant portions of the speech. Moreover, the discriminator can also more accurately enforce constraints on the global speech structure, thus the quality of enhanced speech by generator is better. Experimental results demonstrate that the proposed approach outperforms the competing methods measured by objective quality and speech intelligibility. © 2021 IEEE.

关键词： Generative adversarial networks

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Target detection algorithm with wide field of view in foggy weather based on improved CSLBP operator 4

Target detection algorithm with wide field of view in foggy ...

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4th International Conference on Image processing and Intelligent Control, IPIC 2024

作者： Lian, Xiya Jia, Zhenhong Zhou, Gang Wang, Jiajia Song, Sensen Xiao, Xiaohui Shi, Fei College of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510682313

In order to solve the problem of unclear foreground targets and false foregrounds after performing target detection tasks in foggy video images with wide fields of view, we propose an improved video target detection algorithm for the CSLBP operator to detect foreground targets in different scenes. The improved CSLBP operator is used to extract the texture features of the wide-field image, and the foreground target pixels are judged combined with background modeling. This method is not sensitive to changes in indoor lighting or outdoor sunlight, can effectively solve the shadow problem, and more completely extracts foreground moving targets. It also has obvious effects in bad weather such as foggy weather, and has good robustness. © 2024 SPIE.

关键词： Image enhancement

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BD-YOLO: Optimising Insect Imbalance Data detection Models

BD-YOLO: Optimising Insect Imbalance Data Detection Models

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

作者： Wang, Jian-Yi Jia, Zhen-Hong Zhou, Gang Wang, Jia-Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory Xinjiang University Urumqi830046 China

ISBN: (纸本)9781665410205

Vision-based insect detection is an important task in smart agriculture. Field-collected insect image datasets usually show severe imbalances in categories. Previous models have insufficient accuracy as well as low real-time performance for detecting unbalanced data of insects. To address the issues described above, an improved framework Balance Detect YOLO(BD-YOLO) based on YOLOv8 is proposed in this paper. We innovatively designed Focaler-CPIoU loss to increase the weight of a few classes of samples to achieve the optimisation of pest imbalance data detection. We introduced lightweight convolutional MBConv to improve the recognition accuracy while reducing the number of model parameters. In order to verify the effectiveness of the method in this paper, we established an insect dataset containing six types of insects, in which the most numerous thrips is 431.5 times more than the least numerous chrysopaperla. The experimental results show that compared with YOLOv8, the mAP50 reaches 61.7% with a 73.1% reduction in the number of model parameters, an improvement of 5.4%, and an improvement of 769 in FPS. A feasible approach to optimise the detection of unbalanced number of insects is provided in this paper along with the ability to be deployed on lightweight hardware devices. © 2024 IEEE.

关键词： Invertebrates

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Video image change detection under a wide field of view in foggy weather

Video image change detection under a wide field of view in f...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Li, Wen Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei School of Computer Science and Technology Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

Addressing the problem of poor visual effect and noise interference in foggy video images collected by hawk-eye image sensors, we suggest an approach which utilizes difference images (DIs) fusion and two-stage clustering for detecting changes in monitoring video pictures. First, we utilize a dehazing algorithm based on the color attenuation prior theory to effectively eliminate the haze interference. Then, two DIs are generated using the compressed logarithmic ratio and maximum mean pixel ratio operators. Fusion of two DIs using multi-scale Laplace pyramid and regional energy algorithms, and filtered using the adaptive median filtering algorithm to obtain the final DIs. Finally, the soft threshold function and the FCM clustering algorithm are used to perform two-stage clustering on the DI to obtain the change detection result. Experimental results demonstrate that this approach can effectively and accurately detect subtle changes in hawk-eye surveillance video images under foggy conditions. © 2024 SPIE.

关键词： Image fusion

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