Digital Signal Processing最新文献_第3页

CEMP-YOLO: An infrared overheat detection model for photovoltaic panels in UAVs

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-22 DOI: 10.1016/j.dsp.2025.105072

Yan Hong, Lei Wang, Jingming Su, Yun Li, Shikang Fang, Wen Li, Mushi Li, Hantao Wang

Aiming at the complex working conditions of actual PV power stations, traditional PV panel detection methods employed by operators still result in some faults and safety risks. Under the framework of the YOLOv10n model, a CEMP-YOLOv10n-based infrared image detection algorithm for photovoltaic power plants is proposed. The improvements in CEMP-YOLOv10n comprise four main components. The ABCG_Block structure was designed, and the C2f structure within the Backbone component was optimized to enhance feature extraction capabilities. The ERepGFPN structure is used in the Neck component to retain semantic information and fuse features between high and low layers. The detector head was optimized with PConv convolution to minimize redundant information. Finally, MECA attention was added before P3, P4, and P5 detection heads to enhance adaptive recognition and accuracy.Experimental validation using infrared UAV imagery of PV panels shows the model's computational cost decreased to 4.7 GFLOPs, 72.3 % of the original. Parameters and weights decreased by 25.99 % and 24.13 %, respectively, while accuracy and mean average precision (mAP) improved by 8.3% and 2 %, reaching 86.6 % and 87.3 %. Compared to 13 YOLO-series algorithms, including DETR, YOLOv8n, YOLOv9-tiny, and YOLOv11n, the CEMP-YOLOv10n model demonstrates superior accuracy, parameter efficiency, and memory consumption. The CEMP-YOLOv10n model significantly improves defect recognition accuracy, reduces missed detections, and balances lightweight design with detection speed. This lays the foundation for future UAV inspection edge device deployment and smart PV big data platform creation.

{"title":"CEMP-YOLO: An infrared overheat detection model for photovoltaic panels in UAVs","authors":"Yan Hong, Lei Wang, Jingming Su, Yun Li, Shikang Fang, Wen Li, Mushi Li, Hantao Wang","doi":"10.1016/j.dsp.2025.105072","DOIUrl":"10.1016/j.dsp.2025.105072","url":null,"abstract":"<div><div>Aiming at the complex working conditions of actual PV power stations, traditional PV panel detection methods employed by operators still result in some faults and safety risks. Under the framework of the YOLOv10n model, a CEMP-YOLOv10n-based infrared image detection algorithm for photovoltaic power plants is proposed. The improvements in CEMP-YOLOv10n comprise four main components. The ABCG_Block structure was designed, and the C2f structure within the Backbone component was optimized to enhance feature extraction capabilities. The ERepGFPN structure is used in the Neck component to retain semantic information and fuse features between high and low layers. The detector head was optimized with PConv convolution to minimize redundant information. Finally, MECA attention was added before P3, P4, and P5 detection heads to enhance adaptive recognition and accuracy.Experimental validation using infrared UAV imagery of PV panels shows the model's computational cost decreased to 4.7 GFLOPs, 72.3 % of the original. Parameters and weights decreased by 25.99 % and 24.13 %, respectively, while accuracy and mean average precision (mAP) improved by 8.3% and 2 %, reaching 86.6 % and 87.3 %. Compared to 13 YOLO-series algorithms, including DETR, YOLOv8n, YOLOv9-tiny, and YOLOv11n, the CEMP-YOLOv10n model demonstrates superior accuracy, parameter efficiency, and memory consumption. The CEMP-YOLOv10n model significantly improves defect recognition accuracy, reduces missed detections, and balances lightweight design with detection speed. This lays the foundation for future UAV inspection edge device deployment and smart PV big data platform creation.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"161 ","pages":"Article 105072"},"PeriodicalIF":2.9,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NLoS target localization in IRS-assisted FDA-MIMO radar: A tensor decomposition perspective

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-21 DOI: 10.1016/j.dsp.2025.105093

Weijia Yu , Jianhe Du , Yuanzhi Chen , Shufeng Li , Xingwang Li , Shahid Mumtaz

Intelligent reconfigurable surface (IRS) provides an innovative solution for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar systems in the localization of non-line-of-sight (NLoS) traffic targets. In this paper, we consider an IRS-assisted FDA-MIMO radar system and propose a NLoS multi-target localization algorithm based on tensor decomposition. Specifically, the received signals are first constructed as a third-order tensor model. Then, a sequential minimum description length (MDL) method is employed to estimate the number of targets in advance. With tensor decomposition, the steering matrices containing angle and range information are obtained. In the estimated transmitting steering matrix, the directions-of-departure (DODs) and ranges are successfully decoupled after solving the phase ambiguity. In the estimated receiving steering matrix, a two-dimensional grid search method is applied to obtain the horizontal directions-of-arrival (DOAs) and vertical DOAs. Finally, the localization of NLoS targets is determined by utilizing the geometric relationships of these estimated parameters. Besides, the Cramér-Rao bound (CRB) for the estimations of angle and range is derived as a performance benchmark. Simulation results demonstrate the effectiveness of the proposed algorithm in locating NLoS targets.

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引用次数: 0

Unsupervised learning-based deep sparsifying transform network for joint CT metal artifact reduction and super-resolution reconstruction

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-21 DOI: 10.1016/j.dsp.2025.105092

Shengnan Yan , Yingshuai Zhao , Baoshun Shi , Yueming Su

Due to the presence of metallic implants, metal artifacts degrade the quality of computed tomography (CT) images. Existing deep learning-based metal artifact reduction (DL-based MAR) methods rely on paired datasets, i.e., the ground truth CT image and its metal artifact corrupted version, for training. However, it is difficult to obtain paired datasets in clinical scenarios. Unsupervised learning-based MAR algorithms can use unpaired datasets to address the difficulty of obtaining paired data, but still face the following limitations: (i) most MAR network architectures lack interpretability and exhibit redundant learnable parameters, due to their empirical design nature; (ii) the representation ability of existing encoder-decoder-based network architectures is limited, and they often ignore the image resolution. To overcome these limitations, we introduce an unsupervised learning-based deep sparsifying transform network, dubbed UnDeepST, which is designed for the reconstruction of CT images with both metal artifact reduction (MAR) and super-resolution (SR) capabilities. UnDeepST is model-interpretable and has a smaller number of learnable parameters due to less recycling use of encoder and decoders, compared to previous unsupervised learning-based MAR methods. Furthermore, we design a task fusion module to assist MAR with the help of SR to reconstruct high-quality and high-resolution CT images. To the best of our knowledge, we are the first to merge the MAR and SR tasks to achieve mutual learning of information across different tasks. By designing various loss functions, UnDeepST can be trained on unpaired datasets in an end-to-end training manner. Experimental results demonstrate that UnDeepST can achieve competitive recovery quality and resolution compared to benchmark algorithms.

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引用次数: 0

A novel joint learning framework combining fuzzy C-multiple-means clustering and spectral clustering for superpixel-based image segmentation

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-21 DOI: 10.1016/j.dsp.2025.105083

Chengmao Wu, Pengfei Gai

In recent years, image segmentation algorithms based on superpixels have been continuously developed. However, the superpixel algorithm consists of two independent stages: superpixel generation and superpixel segmentation. When the generation of superpixels is influenced by noise or complex backgrounds, the quality of the generated superpixel image can significantly decline, adversely affecting the subsequent segmentation results. Therefore, this paper proposes a robust multiple-means joint clustering algorithm based on superpixels, which integrates superpixel generation and superpixel image segmentation within a unified learning framework. This approach achieves multiple-means joint clustering by alternately optimizing and updating superpixel and sub-cluster centers. Compared with traditional superpixel segmentation algorithms, this method does not generate superpixels separately and demonstrates improved segmentation performance. Additionally, the algorithm incorporates spectral clustering to transform the superpixel image segmentation problem into a constrained Laplacian matrix rank optimization problem, ultimately achieving clustering based on bipartite graph connectivity, which further enhance the algorithm's robustness. Numerous experimental results indicate that the proposed algorithm yields superior segmentation outcomes compared with existing other superpixel segmentation algorithms and aligns more closely with real-world segmentation details.

近年来，基于超像素的图像分割算法不断得到发展。然而，超像素算法包括两个独立的阶段：超像素生成和超像素分割。当超像素的生成受到噪声或复杂背景的影响时，生成的超像素图像质量会明显下降，从而对后续的分割结果产生不利影响。因此，本文提出了一种基于超像素的鲁棒多均值联合聚类算法，它将超像素生成和超像素图像分割集成在一个统一的学习框架中。这种方法通过交替优化和更新超像素和子簇中心来实现多均值联合聚类。与传统的超像素分割算法相比，该方法无需单独生成超像素，分割性能得到了提高。此外，该算法还结合了光谱聚类技术，将超像素图像分割问题转化为受约束的拉普拉斯矩阵秩优化问题，最终实现了基于两方图连接性的聚类，进一步增强了算法的鲁棒性。大量实验结果表明，与现有的其他超像素分割算法相比，所提出的算法能产生更优越的分割结果，而且更贴近现实世界的分割细节。

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引用次数: 0

Chinese Character Recognition based on Swin Transformer-Encoder

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-21 DOI: 10.1016/j.dsp.2025.105080

Ziying Li , Haifeng Zhao , Hiromitsu Nishizaki , Chee Siang Leow , Xingfa Shen

Optical Character Recognition (OCR) technology, which converts printed or handwritten text into machine-readable text, holds significant application and research value in document digitization, information automation, and multilingual support. However, existing methods predominantly focus on English text recognition and often struggle with addressing the complexities of Chinese characters. This study proposes a Chinese text recognition model based on the Swin Transformer encoder, demonstrating its remarkable adaptability to Chinese character recognition. In the image preprocessing stage, we introduced an overlapping segmentation technique that enables the encoder to effectively capture the complex structural relationships between individual strokes in lengthy Chinese texts. Additionally, by incorporating a mapping layer between the encoder and decoder, we enhanced the Swin Transformer's adaptability to small image scenarios, thereby improving its feasibility for Chinese text recognition tasks. Experimental results indicate that this model outperforms classical models such as CRNN and ASTER on handwritten and web-based datasets, validating its robustness and reliability.

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引用次数: 0

Self-supervised disentangled representation learning with distribution alignment for multi-view clustering

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-21 DOI: 10.1016/j.dsp.2025.105078

Zhenqiu Shu, Teng Sun, Zhengtao Yu

Recently, multi-view clustering has attracted much attention due to its strong capability to fully explore complementary information between multiple views. In general, there may be differences in feature distribution between views from different data sources. However, most existing methods usually directly fuse different views, ignoring the difference in contribution and importance of different views. Thus, it leads to mutual interference between common representation and view-specific information. To address these issues, in this paper, we propose a novel method, called self-supervised disentangled representation learning with distribution alignment (S2DRL-DA), for multi-view clustering. Firstly, the proposed method uses adversarial learning and attention mechanisms to align potential feature distributions and focus on the most critical view. Then the disentangled representation learning is used to separate common and specific representations learned from each view to reduce redundancy in multi-view data. Finally, we adopt KL divergence to assess the quality of the clustering result of each view and guide the model optimization. Extensive experiments on different datasets demonstrate that our S2DRL-DA approach produces competitive performance in multi-view clustering applications. The source code for this work can be found at https://github.com/szq0816/S2DRL-DA.

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引用次数: 0

Robust recovery of nearly-sparse complex signals from phaseless measurements in the presence of noise

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-20 DOI: 10.1016/j.dsp.2025.105073

Shuxian Li , Jiahui Wu , Wenhui Liu, Anhua Wan

Reconstruction of sparse complex signals from quadratic magnitude-only measurements

y_{j} = | 〈 a_{j}, x 〉 |^{2} + z_{j} (j = 1, 2, \dots, m)

is of particular importance in many fields. In this paper, the recovery of nearly k-sparse complex signals from phaseless measurements is examined by

ℓ_{p}

nonconvex minimization method. Sufficient condition is established to guarantee robust and stable recovery of nearly k-sparse signals in different types of noise settings. The new results substantially generalize and improve the state-of-the-art results which focused on the recovery of strictly k-sparse signals from phaseless measurements in noiseless setting.

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引用次数: 0

ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-20 DOI: 10.1016/j.dsp.2025.105075

Liang Wu, Wenjing Lu, Liming Tang, Zhuang Fang

The Retinex theory models the image as a product of illumination and reflection components, which has received extensive attention and is widely used in image enhancement, segmentation and color restoration. However, it has been rarely used in additive noise removal due to the inclusion of both multiplication and addition operations in the Retinex noisy image modeling. In this paper, we propose an exponential Retinex decomposition model based on hybrid non-convex regularization and weak space oscillation-modeling for image denoising. The proposed model utilizes non-convex first-order total variation (TV) and non-convex second-order TV to regularize the reflection component and the illumination component, respectively, and employs weak

H^{- 1}

-norm to measure the residual component. By utilizing different regularizers, the proposed model effectively decomposes the image into reflection, illumination, and noise components. An alternating direction multipliers method (ADMM) combined with the Majorize-Minimization (MM) algorithm is developed to solve the proposed model. Furthermore, we provide a detailed proof of the convergence property of the algorithm. Three publicly available image datasets are used for numerical experiments, and compared with six other classical and state-of-the-art methods, the proposed model exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM).

{"title":"ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application","authors":"Liang Wu, Wenjing Lu, Liming Tang, Zhuang Fang","doi":"10.1016/j.dsp.2025.105075","DOIUrl":"10.1016/j.dsp.2025.105075","url":null,"abstract":"<div><div>The Retinex theory models the image as a product of illumination and reflection components, which has received extensive attention and is widely used in image enhancement, segmentation and color restoration. However, it has been rarely used in additive noise removal due to the inclusion of both multiplication and addition operations in the Retinex noisy image modeling. In this paper, we propose an exponential Retinex decomposition model based on hybrid non-convex regularization and weak space oscillation-modeling for image denoising. The proposed model utilizes non-convex first-order total variation (TV) and non-convex second-order TV to regularize the reflection component and the illumination component, respectively, and employs weak <span><math><msup><mrow><mi>H</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>-norm to measure the residual component. By utilizing different regularizers, the proposed model effectively decomposes the image into reflection, illumination, and noise components. An alternating direction multipliers method (ADMM) combined with the Majorize-Minimization (MM) algorithm is developed to solve the proposed model. Furthermore, we provide a detailed proof of the convergence property of the algorithm. Three publicly available image datasets are used for numerical experiments, and compared with six other classical and state-of-the-art methods, the proposed model exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM).</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"161 ","pages":"Article 105075"},"PeriodicalIF":2.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A pseudo measurement-level arithmetic average fusion in asynchronous networks

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-20 DOI: 10.1016/j.dsp.2025.105089

Yu Xue, Xi-an Feng

Association operation decomposes the multi-target arithmetic average (AA) fusion into multiple groups of single-target merging. The B1 fusion is highly close to the optimal fusion of delayed measurements by computing and handling correlations. Accordingly, a pseudo measurement-level AA (PML-AA) fusion algorithm of Gaussian mixture probability hypothesis density (GM-PHD) filters is proposed to ameliorate the tracking accuracy of asynchronous data by applying the superior B1 fusion as the required merging method. Since the B1 fusion belongs to measurement-level methods, a specified technique is developed to extract measurements contained in locally filtered estimates. As required by the B1 fusion and our measurement extraction technique, a master filter that only operates prediction is introduced to provide indispensable prior estimates. To accommodate this master filter, a hierarchical structure involving a master filter and several local filters is designed. Simulations demonstrate that by virtue of the superiority of the B1 method in fusing delayed data and accurate association, the proposed PML-AA fusion outperforms the existing AA fusion in tracking accuracy within various tracking scenarios.

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引用次数: 0

Geolocation database assisted sub-Nyquist cooperative spectrum sensing for cognitive radio networks

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-02-20 DOI: 10.1016/j.dsp.2025.105091

Aswathy G․P․

Cognitive radio technology emerges as a highly promising solution for optimizing spectrum utilization in next-generation wireless communication systems. Spectrum sensing is crucial for cognitive radio systems, yet implementing Nyquist wideband spectrum sensing poses significant challenges, especially for compact commodity radios with limited energy and computational capabilities. To address these challenges, various sub-Nyquist alternatives have been developed, which overcome the limitations of traditional Nyquist sampling. However, sub-Nyquist techniques, which rely on compressive measurements, are vulnerable to issues such as multipath fading, shadowing, noise uncertainty, and varying channel conditions. To mitigate these issues, a cooperative sensing technique is employed, leveraging the spectral diversity of secondary users. This method enhances detection performance by having secondary users share the recovered spectral support with the master device. The master device then collectively determines the spectrum occupancy status, using the aggregated data to ensure optimal use of available frequencies and reducing the likelihood of interference with licensed users. To further streamline the process and improve detection performance, integrating prior information from a geolocation database is considered, resulting in a hybrid approach. This paper introduces a novel hybrid sub-Nyquist cooperative wideband spectrum sensing technique designed for cognitive radio networks. The primary objectives of this technique include reducing computational and implementation complexity, particularly compared to conventional spectrum sensing schemes. Simulation results validate the efficacy of the proposed hybrid scheme, demonstrating superior detection performance compared to cooperative as well as non-cooperative sensing schemes. This research marks a significant advancement in addressing the challenges of spectrum sensing in cognitive radio networks, offering a more efficient and robust solution for spectrum utilization in dynamic wireless environments.

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引用次数: 0