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A Single-Stage Photovoltaic Module Defect Detection Method Based on Optimized YOLOv8

IEEE ACCESS 2025年 13卷 27805-27817页

作者： Gao, Yihong Pang, Chengxin Zeng, Xinhua Jiang, Pengyi Shanghai Univ Elect Power Shanghai 200000 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China NYU New York NY 10012 USA

Defect detection in photovoltaic (PV) modules presents significant challenges. In the presence of inconspicuous or small-scale defects, downsampling operations can cause features to vanish, and computational complexity increases as the model deepens, resulting in reduced detection accuracy, higher latency, and challenges in deploying the model on edge devices with limited computational resources. In response, this article proposes a single-stage object detection model based on YOLOv8, the PSA-PVdetector (PSA-det). The core innovation of PSA-det is the novel Partial Spatial Attention (PSA) mechanism, which integrates Partial Convolution (PConv) with Spatial Attention (SA) to optimize feature extraction and reduce computational overhead. For small-scale defect detection, a Multi-Channel Feature Fusion (MCFF) detection head is designed to enable finer-grained center prediction. Additionally, ShapeIoU is employed as the bounding box regression metric, enhancing the classical IoU by incorporating bounding box shape, thereby improving defect localization accuracy. Experimental results demonstrate that PSA-det achieves an mAP50 of 87.2% on the Panel-2 dataset and 72.0% on the Solar dataset. On the Nvidia Jetson Xavier NX DK platform, PSA-det achieves an inference latency as low as 2.6 ms, effectively balancing accuracy and real-time performance.

关键词： Feature extraction Defect detection Head YOLO Convolution Accuracy Inspection Computational modeling inference algorithms Termination of employment YOLOv8 photovoltaic defect detection edge deployment partial convolution spatial attention

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ViTeGNN: Towards Versatile Inference of Temporal Graph Neural Networks on FPGA

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第3期36卷 502-519页

作者： Zhou, Hongkuan Zhang, Bingyi Kannan, Rajgopal Busart, Carl Prasanna, Viktor K. Univ Southern Calif Los Angeles CA 90007 USA DEVCOM Army Res Off Los Angeles CA 90089 USA DEVCOM Army Res Lab Adelphi MD 20783 USA

Temporal Graph Neural Networks (TGNNs) are powerful models to capture temporal, structural, and contextual information on temporal graphs, outperforming other methods in many high-impact downstream tasks. However, achieving high-performance TGNN inference in production environments is challenging because TGNN models suffer from high computation complexity and intrinsic temporal data dependency that hinders data parallelism. In addition, real-world TGNN applications have different latency and throughput requirements. This work presents ViTeGNN, a versatile TGNN inference solution for memory-based TGNNs on FPGAs. ViTeGNN performs algorithm-model-architecture co-design to meet the latency and throughput requirements of real-world TGNN applications. Besides the vanilla inference mode ViTeGNN-bal that updates embeddings for nodes interacting with others, we propose ViTeGNN-lat and ViTeGNN-thpt, optimized for latency and throughput. Our model optimizations include a lightweight method to compute attention scores and a related temporal neighbor pruning strategy to reduce computation and memory accesses. These are holistically coupled with key hardware optimizations that leverage the FPGA hardware. We propose a novel hardware module to execute the complex neighbor update process efficiently. To ensure similar accuracy vis-a-vis the original model, the simplified models are trained using the knowledge distillation technique. We propose a unified hardware design that supports all of these three inference modes without FPGA reconfiguration. Enabled by our flexible hardware architecture, we further propose ViTeGNN-auto, which automatically selects the best inference mode at runtime based on latency and throughput requirements, guided by our accurate performance model. We evaluate the performance of the proposed hardware accelerator on five real-world datasets. ViTeGNN-bal reduces the computation complexity by an average of 62% and memory accesses by an average of 36% with only

关键词： Computational modeling Field programmable gate arrays Throughput inference algorithms Optimization Accuracy Parallel processing Heuristic algorithms Graph neural networks Vectors FPGA hardware architecture temporal graph neural network

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A Lightweight Network for Ship Detection in SAR Images Based on Edge Feature Aware and Fusion

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 3782-3796页

作者： Li, Yuming Liu, Jin Li, Xingye Zhang, Xiliang Wu, Zhongdai Han, Bing Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Ship & Shipping Res Inst Co Ltd Shanghai 200135 Peoples R China

Recently, with the increasing adoption of synthetic aperture radar (SAR) ship detection methods on mobile platforms, the lightweighting of detection methods has become a research focus. Despite certain achievements, there are still several limitations: 1) Existing studies have mainly focused on reducing model complexity through shallow network structures. However, this approach frequently results in performance degradation, as they neglected a thorough investigation into achieving a better balance point between inference speed and detection accuracy. 2) Under the lightweight network structure, the rich edge features contained in SAR images, which are crucial for distinguishing ship targets from complex backgrounds, are often underutilized. To address these issues, we propose a novel lightweight detection method based on edge feature aware and fusion. Specifically, to effectively extract edge feature, we introduce an Edge Feature-Aware (EFA) network that incorporates a multiscale channel attention module. Furthermore, a lightweight feature fusion network, Filter-Pruned Bi-directional Feature Pyramid Network (FP-BiFPN), is carefully designed, which can not only suppresses background information, but also accentuates ship targets. Finally, we propose a selective quantization algorithm based on a bit-width selection mechanism to reduce model memory usage without compromising performance. To validate the superiority of our proposed method, we conduct extensive experiments on multiple public datasets, achieving average accuracy scores of 94.2%, 97.6%, and 97.7% on the HRSID, SAR-Ship-Dataset, and SSDD, respectively, with a model parameter size of only 3.36 M, and the fastest processing time for a single frame is 7.2 ms.

关键词： Marine vehicles Feature extraction Image edge detection Radar polarimetry Accuracy Quantization (signal) Synthetic aperture radar Computational complexity Adaptation models inference algorithms Deep learning lightweight network ship detection synthetic aperture radar (SAR)

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Learning From Crowds Using Graph Neural Networks With Attention Mechanism

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IEEE TRANSACTIONS ON BIG DATA 2025年第1期11卷 86-98页

作者： Zhang, Jing Wu, Ming Sun, Zeyi Zhou, Cangqi Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Jiangsu Peoples R China Southeast Univ Engn Res Ctr Blockchain Applicat Supervis & Manage Minist Educ Nanjing 211189 Jiangsu Peoples R China Hohai Univ Sch Artificial Intelligence & Automat Nanjing 211100 Jiangsu Peoples R China Zhejiang Lab Inst Artificial Intelligence Knowledge Engn Res Ctr Hangzhou 311121 Zhejiang Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

Crowdsourcing has been playing an essential role in machine learning since it can obtain a large number of labels in an economical and fast manner for training increasingly complex learning models. However, the application of crowdsourcing learning still faces several challenges such as the low quality of crowd labels and the urgent requirement for learning models adapting to the label noises. There have been many studies focusing on truth inference algorithms to improve the quality of labels obtained by crowdsourcing. Comparably, end-to-end predictive model learning in crowdsourcing scenarios, especially using cutting-edge deep learning techniques, is still in its infant stage. In this paper, we propose a novel graph convolutional network-based framework, namely CGNNAT, which models the correlation of instances by combining the GCN model with an attention mechanism to learn more representative node embeddings for a better understanding of the bias tendency of crowd workers. Furthermore, a specific projection processing layer is employed in CGNNAT to model the reliability of each crowd worker, which makes the model an end-to-end neural network directly trained by noisy crowd labels. Experimental results on several real-world and synthetic datasets show that the proposed CGNNAT outperforms state-of-the-art and classical methods in terms of label prediction.

关键词： Crowdsourcing Noise measurement Data models Biological system modeling Deep learning Task analysis inference algorithms Learning from crowds repeated labeling graph neural networks attention mechanism

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Counterfactual Music Recommendation for Mitigating Popularity Bias

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第2期12卷 851-861页

作者： Yuan, Jidong Gao, Bingyu Wang, Xiaokang Liu, Haiyang Zhang, Lingyin Beijing Jiaotog Univ Key Lab Big Data & Artificial Intelligence Transpo Minist Educ Beijing 100044 Peoples R China Beijing Jiaotog Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Beijing Univ Posts & Telecommun Sch Econ & Management Beijing 100876 Peoples R China

Music recommendation systems aim to suggest tracks that users may enjoy. However, the accuracy of recommendation results is affected by popularity bias. Previous studies have focused on mitigating the direct effect of single-item popularity in video, news, or e-commerce recommendations, but have overlooked the multisource popularity biases in music recommendations. This article proposes a causal inference-based method to reduce the influence of both track and artist popularity. First, we construct a causal graph that encompasses users, tracks, and artists within the context of music recommendations. Next, we employ matrix factorization in conjunction with counterfactual inference theory to mitigate the popularity effects of artists and tracks, taking into account both the natural direct and indirect effects of these entities on music recommendations. Experimental results evaluated on four music recommendation datasets indicate that our method outperforms other baselines and effectively alleviates the popularity bias of both tracks and artists.

关键词： Recommender systems Music Training inference algorithms Electronic commerce Accuracy Measurement Vectors Transportation Tracking loops Counterfactual inference music recommendation popularity bias

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PortLaneNet: Enhancing Port RTG Lane Detection With Explicit Feature Learning Network

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第3期21卷 2154-2163页

作者： Zhang, Jinwei Su, Jinya Yang, Jun Hong, Junming Cao, Yiming Southeast Univ Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU England Shanghai Master Matrix Co Ltd Shanghai 200241 Peoples R China Shanghai East Container Terminal Dept Engn & Technol Shanghai 200080 Peoples R China

Lane detection plays a crucial role in automated driving technology. However, current lane detection algorithms primarily focus on daily scenes, such as cars on urban roads and highways, while limited research addresses closed scenes, such as rubber tire gantries (RTGs) in ports. In this study, we propose a novel lane detection method based on explicit feature learning (EFL) to address this gap. Compared to general scenes, RTG lane lines in port exhibit distinctive characteristics, including regular straight-shaped features, relative spatial stability, and severe wear from heavy RTGs. There is also a higher requirement on algorithm precision while with a limited dataset size. To effectively address these challenges, we proposed PortLaneNet, which introduces an EFL network for better feature extraction, coupled with a shape loss function for improved evaluation and supervision. The multiscale feature extraction of the EFL network improves the model's global perception ability to cope with the loss of features caused by the severe wear of the port lane lines. The improved shape loss function strengthens the linearity of the model's inference results to adapt to the shape characteristics of port lane lines. To improve the model's spatial feature extraction capability, an adaptive channel attention mechanism is also introduced, which embeds spatial positional information of rows. The proposed method is evaluated on a self-collected dataset under various challenging environments, where extensively comparative experiments against six state-of-the-art algorithms with various backbones show that the proposed PortLaneNet demonstrates better detection performance in terms of F1 score, precision, and recall while with computation efficiency suitable for real-time applications.

关键词： Shape Lane detection Feature extraction Representation learning Accuracy Training inference algorithms Global navigation satellite system Adaptive systems Adaptation models Explicit feature learning (EFL) lane detection rubber tire gantry (RTG) shape loss

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Intelligence Evaluating Computational Power: A Multi-Factor Method

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IEEE ACCESS 2025年 13卷 27398-27415页

作者： Huang, Yuhuan Li, Hang Lu, Dongdong Zhang, Zhe Tie, Wenjie Suzhou Key Lab Microwave Imaging Proc & Applicat T Suzhou 215000 Peoples R China Suzhou Aerosp Informat Res Inst Suzhou 215000 Peoples R China Natl Key Lab Microwave Imaging Beijing 100000 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100000 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100000 Peoples R China

With the rapid development of hardware technologies and the continuous optimization of deep learning tasks, accurately evaluating the computational power of artificial intelligence (AI) systems has become a critical requirement. However, the diversity of hardware architectures and the complexity of deep learning tasks have hindered the establishment of a widely accepted unified evaluation standard. To address this issue, we propose a novel evaluation method that integrates key metrics such as model performance, computational throughput, and energy efficiency, providing a more comprehensive and reliable assessment of AI hardware computational capabilities. Through benchmark tests on servers, statistical analysis of the method, and case studies in cloud, edge, and terminal environments, this approach not only ensures the accuracy of existing evaluation frameworks but also reveals the key characteristics and potential influencing factors of hardware computational power from multiple dimensions.

关键词： Computational modeling Benchmark testing Artificial intelligence Training inference algorithms Deep learning Analytical models Performance evaluation Accuracy Predictive models Deep learning benchmark test AI hardware performance evaluation model performance assessment

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Knowledge-Driven Channel Estimation for Asymmetrical Massive MIMO Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 911-924页

作者： Yang, Ruming Xu, Shu Zhu, Zhiming Li, Chunguo Huang, Yongming Yang, Luxi Southeast Univ Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

A novel asymmetrical massive multiple-input multiple-output (MIMO) system has recently emerged as a crucial solution for reducing hardware complexity and alleviating the data processing pressure. However, the absence of channel reciprocity in this system presents unique challenges when directly applying traditional channel estimation methods, inevitably leading to performance loss. Deep learning approaches hold promise for achieving improved channel estimation performance by implicitly learning channel features. Unfortunately, deep learning approaches are often designed empirically. It is critical to utilize prior knowledge to develop an efficient deep neural network (DNN), especially for wireless communication systems. This paper explores a knowledge-driven DNN design approach and introduces a deep learning-based channel estimation framework for asymmetrical transceivers. The channel estimation problem is decoupled into channel denoising and information inference problems. Two novel DNNs are proposed to eliminate noise and exploit correlative features for reconstructing the missing channel information, respectively. Extensive simulations demonstrate that our proposed channel estimation framework can significantly eliminate noise effects, even in low signal-to-noise ratio regimes, and outperform traditional estimators and other deep learning-based methods. Moreover, ablation studies also validate the effectiveness of our knowledge-driven network structure design approach.

关键词： Channel estimation Massive MIMO Receiving antennas Radio frequency Transmitting antennas inference algorithms Noise reduction Asymmetrical transceiver channel estimation channel transfer deep learning uplink-to-downlink

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Vehicle Detection and Tracking Based on Improved YOLOv8

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IEEE ACCESS 2025年 13卷 24793-24803页

作者： Liu, Yunxiang Shen, Shujun Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai 201418 Peoples R China

With the rapid increase in transportation pressure, the demand for efficient traffic recognition and tracking systems is growing. Traditional methods have certain limitations when dealing with complex situations in traffic scenes, such as large weights and insufficient detection accuracies, et al. Therefore, we propose a novel method based on YOLOv8n. Firstly we introduced SCC_Detect based on SCConv on the detection head to reduce the computation of redundant features. Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. The BotSORT tracker is embedded in the period of inference, which achieves more accurate and stable recognition and tracking results in the traffic scene. The experimental results show that the proposed model reduces parameters and weight by approximately 36.5% and 25% respectively at the expense of only 0.2% mAP@0.5 compared with YOLOv8n on the UA-DETRAC dataset. In terms of tracking, MOTA, IDF1 and MOTP of the BoTSORT algorithm on the test video were superior to those of DeepSORT and ByteTrack. The accuracy was improved, and the number of lost tracks was reduced. It has a high practical value and application prospects in traffic detection and deployment.

关键词： Feature extraction Accuracy Convolution Roads Target tracking Kernel Convolutional neural networks YOLO Radar tracking inference algorithms Vehicle tracking multi-object track lightweight model YOLOv8 vehicle detection BotSORT

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Real-Time Task Scheduling With Fairness in Digital Twin Systems

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IEEE INTERNET OF THINGS JOURNAL 2025年第7期12卷 7846-7862页

作者： Kim, Cheonyong Saad, Walid Han, Jonghun Yu, Tao Sakaguchi, Kei Jung, Minchae Korea Natl Univ Transportat Dept Comp Software Chungju 27469 South Korea Virginia Tech Dept Elect & Comp Engn Arlington VA 22203 USA Korea Railrd Res Inst Smart Elect & Signaling Div Train Control & Commun Res Team Uiwang 16105 South Korea Inst Sci Tokyo Dept Elect & Elect Engn Tokyo 1528550 Japan Sejong Univ Dept Elect & Informat Engn Seoul 05006 South Korea

Digital twin (DT) can help create a digital representation of a physical system, thereby reflecting its real-time status. The digital object, often called cyber twin (CT), facilitates real-time monitoring and control of the physical object, i.e., the so-called physical twin (PT). Owing to this ability, CTs can optimize the PTs and simulate their status, without interrupting the physical world. Given the various CT use cases, one can identify two distinct types of DT tasks: 1) update tasks for PT-CT synchronization and 2) inference tasks for obtaining real-time testing responses. The diverse real-time requirements for update/inference tasks raise the task scheduling problem that has been neglected in previous studies. In this article, the real-time DT task scheduling problem is investigated. In particular, a new approach for evaluating the performance of real-time scheduling of DT tasks is introduced considering the relationship between update/inference tasks and fairness among CTs. Moreover, offline and online DT task scheduling schemes are proposed with the goals of maximizing the DT freshness ratio and minimizing task rejections. In particular, the DT freshness ratio maximization problem is formulated as an offline task scheduling scheme. The proposed offline solution can significantly reduce the solution space without losing optimality. Furthermore, the scheduling policies for achieving the maximal DT freshness ratio are established using which an online scheduling algorithm is designed. Simulation results show that the proposed offline/online schemes increase the DT freshness ratio by at least 16% and 11%, respectively, compared to benchmarks. The results also show that the task rejection ratio of the proposed online algorithm is within 8% of the lower bound.

关键词： Real-time systems Job shop scheduling Testing Optimization Internet of Things Digital twins Synchronization inference algorithms Computational modeling Accuracy Digital twin (DT) fairness offline and online algorithm real-time task scheduling

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