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3rd International Conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Wang, Shouman Gong, Zhaoguang Zhang, Zhijia Wang, Shixian Zhao, Huaici School of Artificial Intelligence Shenyang University of Technology Shenyang China Shenyang Key Laboratory of Intelligent Perception and Edge Computing Shenyang China National University Science Park Shenyang University of Technology Shenyang China Liaoning Provincial Key Laboratory of Optoelectronic Information and Simulation Technology Shenyang China

ISBN: (纸本)9781510670945

Binocular vision is an important branch of computer vision that processes images captured from two different cameras with different perspectives to obtain depth information of a scene. However, the stereo matching algorithm is complex and traditional platforms often struggle to meet real-time and accuracy requirements simultaneously. In this article, we implement a binocular vision system based on the idea of software-hardware co-design on the Zynq platform. According to the characteristics of the Zynq platform, the system's software and hardware functions are divided. The processing system (PS) is mainly responsible for flow control and binocular correction, while the programmable logic (PL) is responsible for parallel acceleration of the stereo matching algorithm. In the image acquisition part, we configure the format and output image timing using a designed camera acquisition module. In the stereo matching part, we design a semi-global stereo matching algorithm using Verilog language. Finally, we build a binocular vision system on the Zynq platform for testing. The experimental results show that the frame rate can reach 30fps when the input image resolution is 640×480, which satisfies the requirements of real-time and accuracy of the output disparity map. © 2023 SPIE.

关键词： Computer hardware description languages

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ACP- based Circular target image Rotation normalization system 4

ACP- based Circular target image Rotation normalization syst...

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4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： He, Jichang Zhang, Zhijia Zhao, Huaici Yang, Jiale School of Artificial Intelligence Shenyang Key Laboratory of Information Perception and Edge Computing Shenyang University of Technology Liaoning Shenyang China Shenyang Institute of Automation Liaoning Provincial Key Laboratory of Optoelectronic Information and Simulation Technology Liaoning Shenyang China

ISBN: (纸本)9798350326444

In certain visual detection tasks that focus on circular objects, especially for those with less distinctive features, the rotation of the circular object in the image often leads to significant errors in the detection results or even task failure. To adaptively locate the stable rotation normalization position of circular object images, this paper designs a circular object image rotation normalization system based on the Adaptive Circle Projection (ACP) algorithm, which can adaptively detect and rotate any circular object image to this position regardless of the rotation angle. Experimental results on multiple types of different objects show that the system can adaptively detect stable rotation normalization positions for 10 circular objects with any rotation angle. This paper also designs an image operation interface for the system to demonstrate its portability. © 2023 IEEE.

关键词： Rotation

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Oilfield Staff Workwear Detection and Monitoring System 2nd

Oilfield Staff Workwear Detection and Monitoring System

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2nd World Conference on intelligent and 3D Technologies, WCI3DT 2023

作者： Guo, Yuting Zhang, Zhijia Liu, YunPeng Wang, Shixian School of Artificial Intelligence Shenyang University of Technology Shenyang Liaoning110870 China Shenyang Key Laboratory of Information Perception and Edge Computing Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Beijing China Liaoning Provincial Key Laboratory of Optoelectronic Information and Simulation Technology Liaoning Province China

ISBN: (纸本)9789819721436

In this paper, deep learning algorithms are applied to oilfield work scenarios, and YOLOv5s is selected as the backbone network for staff workwear detection and analysis of existing problems: To improve the small-scale objective detection capability, the second layer of the backbone network is added for feature output and the SimAM attention mechanism module is introduced. To improve the obscuration objective detection capability, the G-Transformer encoder module is introduced, and the Weighted boxes fusion (WBF) candidate frame screening method is selected for post-processing. After that, the above-improved method is designed in general, and the G-YOLOv5s detection network is proposed. Experiments show that G-YOLOv5s improves detection accuracy by 5.31% compared to the initial network, while the speed is only reduced by 20 frames/second. Finally, the PyQt tool with "OpenCV library + frame loop" as the core control method for monitoring system development can realize real-time monitoring of workers’ dressing in oilfield scenes, which has high practical value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Signal encoding

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Target visual detection and positioning for autonomous driving 9

Target visual detection and positioning for autonomous drivi...

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9th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Yang, Li Zhang, Zhijia Zhao, Huaici Wang, Shixian Zhao, Yongmao Shenyang University of Technology School of Artificial Intelligence Shenyang110870 China Shenyang Key Laboratory of Information Perception and Edge Computing Shenyang China Liaoning Provincial Key Laboratory of Optoelectronic Information and Simulation Technology Shenyang China Shenyang Meixing Technology Co Ltd. Shenyang China

ISBN: (纸本)9798350304428

In response to the problems of low accuracy, high computational complexity, and low real-time performance in the binocular visual positioning for autonomous driving, an improved binocular visual positioning algorithm suitable for autonomous driving was proposed by modifying the ORB algorithm based on feature point matching. In the improved positioning algorithm, the YOLOv7 network was used for target detection to obtain the target area, and the Shi-Tomasi algorithm and the GMS algorithm were introduced to improve the S-G-ORB algorithm for stereo matching in the target area to obtain disparity and calculate the three-dimensional coordinates. Experimental results showed that the proposed algorithm has high accuracy and real-time performance, which meets the requirements of target detection and positioning for autonomous driving. © 2023 IEEE.

关键词： Binoculars

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ACP- based Circular target image Rotation normalization system

ACP- based Circular target image Rotation normalization syst...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Jichang He Zhijia Zhang Huaici Zhao Jiale Yang Shenyang Key Laboratory of Information Perception and Edge Computing School of Artificial Intelligence Shenyang University of Technology Shenyang Liaoning China Liaoning Provincial Key Laboratory of Optoelectronic Information and Simulation Technology Shenyang Institute of Automation Shenyang Liaoning China

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Enhancing Stress Detection Accuracy: A CNN-LSTM Model for ECG Signal Analysis

Enhancing Stress Detection Accuracy: A CNN-LSTM Model for EC...

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Sensors and Information Technology (ICSI), International Conference on

作者： Aleksandra Nazarova Artem Obukhov Zhijia Zhang Miao Liu Automated Systems for College of Artificial Intelligence Shenyang University of Technology Shenyang China Automated Systems for Decision-Making Support Tambov State Technical University Tambov Russia Shenyang Key Laboratory of Information Perception and Edge Computing College of Artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Stress detection using physiological signals is crucial for modern healthcare and well-being systems, particularly given the increasing prevalence of stress-related disorders. A major challenge in multi-class stress classification using ECG is the significant overlap in physiological characteristics between different stress states, making clear differentiation difficult due to the high variability of human physiological responses. This study addresses this issue through the selection of the most informative ECG features and the development of a hybrid neural network architecture. The proposed CNN-LSTM architecture consists of two main blocks: a convolutional block with two Conv2D layers, and a recurrent block with two consecutive LSTM layers. This architecture optimally processes 14 carefully selected ECG parameters from the time-domain (7 features), frequency-domain (4 features), and nonlinear-domain (3 features) analyses, utilizing spatial and regular dropout mechanisms to prevent overfitting. Using the publicly available WESAD dataset, the model achieves an overall accuracy of 97% in classifying three states: no stress, amusement, and stress. Notably, the minimal error rate between no-stress and high-stress states (less than 1%) makes the system highly reliable for practical applications, providing an effective single-sensor approach for real-time stress monitoring.

关键词： Accuracy Neural networks Electrocardiography Real-time systems Convolutional neural networks Biomedical monitoring Time-domain analysis Signal analysis Stress Long short term memory

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Target visual detection and positioning for autonomous driving

Target visual detection and positioning for autonomous drivi...

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IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Li Yang Zhijia Zhang Huaici Zhao Shixian Wang Yongmao Zhao School of Artificial Intelligence Shenyang University of Technology Shenyang China Shenyang Key Laboratory of Information Perception and Edge Computing Shenyang China Liaoning Provincial Key Laboratory of Optoelectronic Information and Simulation Technology Shenyang China Shenyang Meixing Technology Co. Ltd. Shenyang China

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Spatial-frequency fusion for retinal vessel segmentation

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Biomedical Signal Processing and Control 2025年 110卷

作者： Weiwei Song Ming Xu Haixing Li Xiaosheng Yu Shenyang University of Technology Shenyang 110020 Liaoning China Shenyang Key Laboratory of Information Perception and Edge Computing Liaoning China Northeastern University Shenyang 110170 Liaoning China

The segmentation of retinal blood vessels is clinically significant for diagnosing many ocular disorders and can assist in identifying multiple medical conditions, including diabetes, atherosclerosis, and cardiovascular disease. Therefore, accurate identification of the retinal blood vessels in the fundus can significantly aid physicians in diagnosing and treating their patients’ conditions. In this paper, we propose a retinal blood vessel segmentation method that combines the spatial and frequency domains. Existing CNN methods obtain local features by using convolutional operations in the spatial domain, and are not capable enough in obtaining global spatial feature information. Therefore, we introduce a Fourier transform to obtain global information and learn the long-distance distribution of blood vessels. In the frequency domain, we designed a multiscale Gaussian high-pass filter to adaptively enhance the edge features of blood vessels of different scales. Since frequency domain information is more concerned with global dependencies and spatial information is more capable of capturing local detailed features, the fusion of frequency and spatial domains can effectively capture the general trends and complex details within the hidden layer space. In order to assess the model’s efficacy, we conducted tests using the pre-existing DRIVE and CHASE_DB1 datasets. Our accuracy achieved 96.90 and 97.81 respectively, and a sensitivity of 83.80 was obtained for the DRIVE dataset. By observing the segmented image, our segmentation is more accurate, clearer, and noise-free than the results of other proposed methods.

关键词： Frequency domain analysis

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