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Research on Early Warning and Monitoring systems for Contact Between Live Equipment and Foreign Objects

IET image processing 2025年第1期19卷

作者： Yuan, Fang Chen, BoYuan Tan, YunFei Liao, KaiYang xia, WeiMin State Grid Shaanxi Electric Power Company Limited Xi'an Power Supply Company of State Grid Shaanxi Electric Power Co. Ltd. Xi'an China Xi'an University of Technology School of Printing Packaging and Digital Media Xi'an University of Technology Xi'an China

Addressing the safety hazards arising from inadequate clearance between tree growth and power facilities, this paper innovatively proposes an online monitoring and early warning system for distribution networks based on advanced image processing technology. The system integrates three core functions: automatic tree species identification, precise detection of safety distances between tree crowns and live equipment, and real-time foreign object contact warnings. By deploying maintenance-free online monitoring terminals, the system can monitor tree growth around the distribution network around the clock, continuously. It utilizes efficient backend algorithms to intelligently analyse the collected image data, promptly detecting and warning of potential tree-obstacle hazards. Compared to traditional manual inspections and UAV inspections, this system not only significantly improves monitoring accuracy and real-time performance but also overcomes the limitations of UAV inspections, such as high costs and poor real-time performance. The solution proposed in this paper is expected to fundamentally enhance the safe operation level of distribution networks and effectively reduce power accidents such as tripping and short circuits caused by tree obstacles. Future plans include further optimizing system functions to promote the wider application and promotion of the technology. © 2025 The Author(s). IET image processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Distance measurement

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Chaos Theory Based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

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A Novel Saliency-Noise Multi-Feature Fusion for Music Signal Recognition Algorithm

A Novel Saliency-Noise Multi-Feature Fusion for Music Signal...

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2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Zhang, TingTing School of Music Shaanxi Vocational Academy of Art Xi'an710054 China

ISBN: (纸本)9798331509828

The rapid development of signal processing (SP) technology, especially the continuous innovation of audio signal recognition and analysis algorithms, has led to a wide variety of application areas. Under this background, this study proposes the novel saliency-noise multi-feature fusion framework for music signal recognition algorithm, which aims to improve the recognition performance of music signals. Initially, the proposed framework extracts the emotional features of music from low-level and mid-level emotional features as the basis for overall modeling. This framework innovatively uses the eGeMAPS feature set as the low-level features, and combines the MIRToolbox tool to extract mid-and high-level music emotion features as a strong support for overall feature extraction. Subsequently, a new MFCC is designed and proposed for musical spectral modeling and generates Hilbert spectrogram through Hilbert-Huang transform. Subsequently, audio saliency detection is performed. During the detection process, the adaptive adjustment to different input signals is achieved through a two-step architecture of dynamic saliency prediction network and channel-level weighting module. Finally, the music rhythm detection is realized by constructing a histogram based on the beat position. In terms of model implementation, the sparse representation classification model is used to realize the classification and recognition of music signals. In order to test the effectiveness of the proposed algorithm, this study used a self-collected data set to experimentally verify the model. The proposed algorithm is superior to the existing Transformer and CNN models in terms of accuracy, precision, recall, and F1 value. The comparative experiment verifies the effectiveness of the proposed algorithm. © 2025 IEEE.

关键词： Hilbert-Huang transform

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Robust self-weighting multi-view fuzzy clustering for image segmentation

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Journal of Intelligent & Fuzzy systems 2025年第1-2期48卷 151-167页

作者： Cai, xiumei Jing, Huarong Wu, Chengmao Song, Liping School of Automation Xi’an University of Posts and Telecommunications Xi’an China School of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an China Jilin General Aviation Vocational and Technical College Jilin China

Traditional single-view clustering cannot process multi-view data well, so multi-view clustering is proposed. However, existing multi-view clustering algorithms have shortcomings in view information extraction, weight parameter selection, and noisy data processing. To overcome these drawbacks, we proposed a robust multi-view fuzzy clustering algorithm based on self-weighting that can handle noisy image segmentation. The algorithm first introduces the consistent fuzzy division matrix and balance coefficient into the general model to actively learn common and individual information among different views; secondly, it adopts a self-weighting strategy to judge the importance degree of each view for clustering; and finally, we construct fuzzy factors for each view based on the local space information and map the improved multi-view algorithm to the kernel space, thereby enhancing its robustness. In addition, we use an alternating optimization method to solve the optimization model and Zangwill’s theorem to prove the convergence of our algorithm. The algorithm has optimal evaluation metrics in the segmentation tasks of synthetic, natural, and remote sensing images, where the ACC values are all greater than 0.91 and the PSNR values are all greater than 20, giving superior segmentation capability and interference resistance compared to several comparative algorithms.

关键词： Multi-view fuzzy clustering balance coefficient self-weighting local space information kernel space

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A Novel Visual Communication Scenario Based on image processing algorithms

A Novel Visual Communication Scenario Based on Image Process...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Jinhua Yang Li Hou Xijing University Shaanxi Xian China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on image processing algorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REVISED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio.

关键词： Training Adaptation models image coding Machine learning algorithms Visual communication image synthesis Heuristic algorithms Semantics Generative adversarial networks Neural style transfer

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Performance Analysis of Low-Cost Solid-State LiDAR for Building Elements Point-Cloud Mapping 2nd

Performance Analysis of Low-Cost Solid-State LiDAR for Build...

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2nd International conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Lo, Ying Cao, Yun Zhou, xingyang Zhang, Cheng Chen, Min Department of Civil Engineering Xi’an Jiaotong-Liverpool University Suzhou China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9789819639489

Accurate point cloud data is vital for the up-to-date representation of buildings. The use of Terrestrial Laser Scanners (TLS) has been prevalent for generating precise point clouds in the built environment. However, despite the development of scan planning algorithms to address issues such as occlusions and varying point densities, the significant costs and extensive time required for data acquisition and processing—often worsen by the lack of existing plans for scan planning—limit the practicality and cost-effectiveness of TLS. This necessitates the investigation of alternative methods for point cloud data collection. This study assesses the capabilities and limitations of low-cost solid-state LiDAR sensors, specifically the Livox Mid-360, in mapping the built environment. The sensor’s performance was benchmarked against TLS and onsite measurements, testing its mapping accuracy over distances ranging from 2 m to 8 m and on different materials including steel, wood, plastic, and concrete. The results demonstrate that the sensor achieves sub-centimeter accuracy within a 2 m range, with the highest accuracy on wood and the least on steel. These results validate the efficacy of low-cost solid-state LiDAR sensors for building modeling requiring centimeter-level accuracy, offering a more affordable and accessible solution for updating Building Information Modelling (BIM) model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mapping

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Deep Learning Based Crack Detection Method for Bridges

Deep Learning Based Crack Detection Method for Bridges

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Electronics, Energy systems and Power Engineering (EESPE), IEEE International conference on

作者： Qinxue Wu Ruida Han Chang'an University Xi'an China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

In this paper, we focus on the problem of bridge crack detection. In view of the characteristics of bridge cracks, such as the existence of multiple scales and shapes, and the interference of complex backgrounds and different illumination conditions on the surface of bridges, we select the bridge crack images covering different scales, types, and illumination conditions to construct a dataset, and then carry out the image enhancement processes, such as rotating, randomly erasing, and adjusting the brightness and contrast of the cracks. Three deep learning algorithms, ResNet18, MobileNetV2 and EfficientNetB0, are selected for the experiments, and the performance of the models is evaluated by comparing the F1 scores, recall rates, confusion matrices, and observed loss curves. The results show that the proposed data processing and deep learning modeling strategy is effective in bridge crack detection, and the classification effect reaches a high level, providing a feasible method for bridge crack detection.

关键词： Bridges Deep learning Power engineering Shape Lighting Interference Robustness Surface cracks image enhancement Surface treatment

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Medical image Segmentation Techniques: Advances and Challenges

Medical Image Segmentation Techniques: Advances and Challeng...

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International image processing, Applications and systems conference (IPAS)

作者： Hela Boulehmi Rania Linda Filali ISET de Bizerte DGET Bizerta Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging.

关键词： Deep learning image segmentation Thresholding (Imaging) Machine learning algorithms image edge detection Focusing Feature extraction Convolutional neural networks Medical diagnostic imaging Active contours

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Application of Various Novel Computer image processing (CIP) algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： xian Du Shanghai Institute of Visual Arts Shanghai China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm.

关键词： Training image coding Art image analysis Translation image edge detection Digital images Semantics Media Feature extraction

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A Route Recommendation Algorithm Based on Multi-Dimensional Data Fusion and Analysis of its Social Utility 3

A Route Recommendation Algorithm Based on Multi-Dimensional ...

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3rd International conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Dai, Yazhe Li, You College of Electrical and Information Engineering JiLin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798331508456

The current route recommendation algorithm has problems such as single data. This paper designs a route recommendation algorithm based on multidimensional data fusion, uses convolutional neural network (CNN) to extract the features of user behavior data, geographic location information and environmental factor data, and then uses the attention mechanism to realize the fusion processing of multidimensional data. Based on the fused multidimensional data features, collaborative filtering and content-based recommendation algorithms are used to generate personalized route recommendations in combination with user preferences and real-time traffic conditions. Experiments have shown that the recommendation accuracy of this algorithm can reach up to 100%under controlled test conditions. Compared with the Bellman-Ford algorithm, this algorithm takes an average of 5.46 minutes less time, and the adoption rate of the recommendation results of this algorithm is as high as 99.9%, which proves the effectiveness of the algorithm in the cultural and tourism route recommendation scenario. © 2025 IEEE.

关键词： image fusion

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