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学者姓名：杨长才
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Addressing the challenges of variable target morphology, small critical regions, and complex background interference in eggplant picking point detection within complex agricultural scenarios, this study proposes MDAD-YOLO (Multi-dimensional Attention and DySample YOLO), a detection model improved based on the YOLOv10n-pose framework. First, the model's cross-dimensional perception ability for fruits and picking points is enhanced by integrating the collaborative mechanism of regional receptive field attention with channel-space joint attention. Next, within the Neck structure, coordinate attention is incorporated to optimize the spatial localization accuracy of fine-grained features, enhancing sensitivity to minute regions such as the fruit stem apex. Additionally, dynamic pixel reorganization is applied to enhance feature map reconstruction details, addressing the detail loss caused by traditional interpolation methods. Finally, cascading adaptive fine-grained channel attention with position-sensitive attention enables multi-level modeling of channel dependencies and collaborative spatial context enhancement. Through a seven-tier validation framework, the model's effectiveness, robustness, and generalizability have been comprehensively demonstrated. Experimental results show that the model achieves 93.6% mAP@50 for object detection, 94.7% mAP@50 and 92.1% mAP for keypoints detection, and an average pixel Euclidean distance error of 19.41 on the self-built eggplant dataset, outperforming YOLOv12 and other high-performance models. Additionally, cross-crop experiments on the pepper dataset showed a 2.1% and 2.7% improvement in mAP for object and picking point detection, respectively, compared to the baseline model, confirming its cross-crop robustness. This study reveals the synergistic enhancement of dynamic upsampling and attention mechanisms in agricultural object detection, providing new insights for lightweight model design in complex scenarios.
Keyword ：
Dynamic upsampling Dynamic upsampling MDAD-YOLO MDAD-YOLO Multi-dimensional attention mechanism Multi-dimensional attention mechanism Object detection Object detection Picking point detection Picking point detection
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| GB/T 7714 | Huang, Yikun , Li, Gang , Li, Jinghu et al. Accurate localization of fruit targets and picking points with multi-dimensional attention and dynamic upsampling [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2026 , 240 . |
| MLA | Huang, Yikun et al. "Accurate localization of fruit targets and picking points with multi-dimensional attention and dynamic upsampling" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 240 (2026) . |
| APA | Huang, Yikun , Li, Gang , Li, Jinghu , Chen, Hao , Lin, Hefei , Yang, Changcai et al. Accurate localization of fruit targets and picking points with multi-dimensional attention and dynamic upsampling . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2026 , 240 . |
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Correspondence pruning aims to identify inliers from an initial set of correspondences with a low inlier ratio. Current Graph Neural Networks (GNNs) based correspondence pruning approaches suffer from feature over-smoothing during information propagation, making it difficult to distinguish inliers from outliers. In addition, Transformer-based methods can model long-range dependencies, but their quadratic complexity limits computational efficiency. To address these issues, we propose MatchMamba, a dual-view correspondence pruning network based on a selective state space model, Mamba. MatchMamba combines the strengths of GNNs and Mamba, enhancing local feature extraction while modeling global context with appropriate complexity. Specifically, to overcome Mamba's limitations in correspondence pruning, such as the lack of local context and unidirectional modeling, we introduce the Cluster Sampling Spatial Mamba (CSSM) block and Correspondence Flip Bidirectional Mamba (CFBM) block. CSSM captures fine-grained local context through the implicit soft assignment and mitigates GNN's over-smoothing using Mamba's selective mechanism. CFBM block leverages Mamba's efficient long-sequence modeling by constructing a pseudo-sequential structure through clustering. It applies forward and backward scanning to enable each correspondence to fully capture contextual information from others, achieving global context modeling with appropriate computational cost. Extensive experiments demonstrate that MatchMamba outperforms current state-of-the-art methods on several challenging tasks. The code is available at https://github.com/Mrwyb/MatchMamba
Keyword ：
Complexity theory Complexity theory Computational efficiency Computational efficiency Computational modeling Computational modeling Context modeling Context modeling Correspondence pruning Correspondence pruning Data models Data models Deep learning Deep learning Feature extraction Feature extraction Forestry Forestry graph neural networks (GNNs) graph neural networks (GNNs) image matching image matching Mathematical models Mathematical models selective state space model selective state space model Transformer Transformer Transformers Transformers
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| GB/T 7714 | Wu, Yubin , Li, Xiaojie , Chen, Hao et al. MatchMamba: Correspondence Pruning via Selective State Space Model [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2026 , 36 (1) : 161-174 . |
| MLA | Wu, Yubin et al. "MatchMamba: Correspondence Pruning via Selective State Space Model" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 36 . 1 (2026) : 161-174 . |
| APA | Wu, Yubin , Li, Xiaojie , Chen, Hao , Yang, Changcai , Wei, Lifang , Chen, Riqing . MatchMamba: Correspondence Pruning via Selective State Space Model . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2026 , 36 (1) , 161-174 . |
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Incident response plays a pivotal role in mitigating the impact of cyber attacks. In recent years, the intensity and complexity of global cyber threats have grown significantly, making it increasingly challenging for traditional threat detection and incident response methods to operate effectively in complex network environments. While Large Language Models (LLMs) have shown great potential in early threat detection, their capabilities remain limited when it comes to automated incident response after an intrusion. To address this gap, we construct an incremental benchmark based on real-world incident response tasks to thoroughly evaluate the performance of LLMs in this domain. Our analysis reveals several key challenges that hinder the practical application of contemporary LLMs, including context loss, hallucinations, privacy protection concerns, and their limited ability to provide accurate, context-specific recommendations. In response to these challenges, we propose IRCopilot, a novel framework for automated incident response powered by LLMs. IRCopilot mimics the three dynamic phases of a real-world incident response team using four collaborative LLM-based session components. These components are designed with clear divisions of responsibility, reducing issues such as hallucinations and context loss. Our method leverages diverse prompt designs and strategic responsibility segmentation, significantly improving the system’s practicality and efficiency. Experimental results demonstrate that IRCopilot outperforms baseline LLMs across key benchmarks, achieving sub-task completion rates of 150%, 138%, 136%, 119%, and 114% for various response tasks. Moreover, IRCopilot exhibits robust performance on public incident response platforms and in real-world attack scenarios, showcasing its strong applicability. Copyright © 2025, The Authors. All rights reserved.
Keyword ：
Automation Automation Benchmarking Benchmarking Complex networks Complex networks Computer crime Computer crime Distributed computer systems Distributed computer systems Intrusion detection Intrusion detection Network security Network security
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| GB/T 7714 | Lin, Xihuan , Zhang, Jie , Deng, Gelei et al. IRCopilot: Automated Incident Response with Large Language Models [J]. | arXiv , 2025 . |
| MLA | Lin, Xihuan et al. "IRCopilot: Automated Incident Response with Large Language Models" . | arXiv (2025) . |
| APA | Lin, Xihuan , Zhang, Jie , Deng, Gelei , Liu, Tianzhe , Liu, Xiaolong , Yang, Changcai et al. IRCopilot: Automated Incident Response with Large Language Models . | arXiv , 2025 . |
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Removing outliers is of paramount importance in the process of feature-based point cloud registration. However, it is still extremely challenging due to the high proportion of outliers, and the estimation of the accurate transmission matrix depends on the distribution of the inliers. The effective extraction of contextual information (local view) and the acquisition of full structural information (global view) influence the identification of inliers. Inspired by Gestalt principles in handling local and global relationships, we propose a Gestalt-inspired Graph Transformer Network (G-GTNet) for robust point cloud registration. G-GTNet extracts broader and more reliable contextual information while effectively aggregating both local and global features. Specifically, adhering to Gestalt principles, we design a multi-granularity aggregation (MGA) block that refines feature maps through a cascaded expanding path to acquire contextual details and promote information exchange among correspondences. In addition, to establish a consensus mechanism between local and global information, we introduce a global consensus attention (GCA) block. Similarly, the GCA follows Gestalt principles to optimally integrate local details and information about global structure, which allows it to gather information on a larger scale. Furthermore, a dependable seed selection (DSS) block is designed to filter out reliable and evenly distributed correspondences by distinguishing outliers and inliers more efficiently. Extensive experiments demonstrate that G-GTNet achieves better performance than state-of-the-art methods. It exhibits competitive performance and robustness in both outlier removal and pose estimation tasks across various public datasets with diverse feature descriptors. Notably, our proposed G-GTNet achieves an RR of 84.36% on the 3DMatch using FPFH descriptor, surpassing SC2-PCR by 1.33%. Our code will be released at https://github.com/gwk429/G-GTNet.
Keyword ：
Gestalt principles Gestalt principles Global consensus attention Global consensus attention Graph transformer Graph transformer Multi-granularity aggregation Multi-granularity aggregation Outlier removal Outlier removal Point cloud registration Point cloud registration
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| GB/T 7714 | Gu, Weikang , Han, Mingyue , Xue, Li et al. G-GTNet: Gestalt-inspired graph transformer network for robust point cloud registration [J]. | KNOWLEDGE-BASED SYSTEMS , 2025 , 329 . |
| MLA | Gu, Weikang et al. "G-GTNet: Gestalt-inspired graph transformer network for robust point cloud registration" . | KNOWLEDGE-BASED SYSTEMS 329 (2025) . |
| APA | Gu, Weikang , Han, Mingyue , Xue, Li , Yu, Jiaming , Dong, Heng , Yang, Changcai et al. G-GTNet: Gestalt-inspired graph transformer network for robust point cloud registration . | KNOWLEDGE-BASED SYSTEMS , 2025 , 329 . |
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Deep learning has been widely applied to high-dimensional hyperspectral image classification and has achieved significant improvements in classification accuracy. However, most current hyperspectral image classification networks follow a patch-based learning framework, which divides the entire image into multiple overlapping patches and uses each patch as input to the network. Such locality-based methods have limitations in capturing global contextual information and incur high computational costs due to patch overlap. To alleviate these issues, we propose a global learning network with a large receptive fields network (GLNet) to capture more comprehensive and accurate global contextual information, thereby enriching the underlying feature representation for hyperspectral image classification. The proposed GLNet adopts an encoder-decoder architecture with skip connections. In the encoder phase, we introduce a large receptive field context exploration (LRFC) block to extract multi-scale contextual features. The LRFC block enables the network to enlarge the receptive field and capture more spectral-spatial information. In the decoder phase, to further extract rich semantic information, we propose a multi-scale simple attention (MSA) block, which extracts deep semantic information using multi-scale convolution kernels and fuses the obtained features with SimAM. Specifically, on the IP dataset, GLNet achieved overall accuracies (OA) of 98.72%, average accuracies (AA) of 98.63%, and Kappa coefficients of 98.3%; similar improvements were observed on the PU and HOS18 datasets, confirming its superior performance compared to baseline models. The experimental results demonstrate that GLNet performs exceptionally well in hyperspectral image classification tasks, particularly in capturing global contextual information. Compared to traditional patch-based methods, GLNet not only improves classification accuracy but also reduces computational complexity. Future work will further optimize the model structure, enhance computational efficiency, and explore its application potential in other types of remote sensing data.
Keyword ：
global contextual global contextual hyperspectral image classification hyperspectral image classification large receptive fields large receptive fields multi-scale fusion multi-scale fusion spatially separable convolution spatially separable convolution
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| GB/T 7714 | Dai, Mengyun , Liu, Tianzhe , Lin, Youzhuang et al. GLN-LRF: global learning network based on large receptive fields for hyperspectral image classification [J]. | FRONTIERS IN REMOTE SENSING , 2025 , 6 . |
| MLA | Dai, Mengyun et al. "GLN-LRF: global learning network based on large receptive fields for hyperspectral image classification" . | FRONTIERS IN REMOTE SENSING 6 (2025) . |
| APA | Dai, Mengyun , Liu, Tianzhe , Lin, Youzhuang , Wang, Zhengyu , Lin, Yaohai , Yang, Changcai et al. GLN-LRF: global learning network based on large receptive fields for hyperspectral image classification . | FRONTIERS IN REMOTE SENSING , 2025 , 6 . |
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The accurate identification of high-quality correspondences is a prerequisite task in feature-based point cloud registration. However, it is extremely challenging to handle the fusion of local and global features due to feature redundancy and complex spatial relationships. Given that Gestalt principles provide key advantages in analyzing local and global relationships, we propose a novel Gestalt-guided Parallel Interaction Network via orthogonal geometric consistency (GPI-Net) in this paper. It utilizes Gestalt principles to facilitate complementary communication between local and global information. Specifically, we introduce an orthogonal integration strategy to optimally reduce redundant information and generate a more compact global structure for high-quality correspondences. To capture geometric features in correspondences, we leverage a Gestalt Feature Attention (GFA) block through a hybrid utilization of self-attention and cross-attention mechanisms. Furthermore, to facilitate the integration of local detail information into the global structure, we design an innovative Dual-path Multi-Granularity parallel interaction aggregation (DMG) block to promote information exchange across different granularities. Extensive experiments on various challenging tasks demonstrate the superior performance of our proposed GPI-Net in comparison to existing methods. The code will be released at https://github.com/gwk/GPI-Net. Copyright © 2025, The Authors. All rights reserved.
Keyword ：
Geometry Geometry
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| GB/T 7714 | Gu, Weikang , Han, Mingyue , Xue, Li et al. GPI-Net: Gestalt-Guided Parallel Interaction Network via Orthogonal Geometric Consistency for Robust Point Cloud Registration [J]. | arXiv , 2025 . |
| MLA | Gu, Weikang et al. "GPI-Net: Gestalt-Guided Parallel Interaction Network via Orthogonal Geometric Consistency for Robust Point Cloud Registration" . | arXiv (2025) . |
| APA | Gu, Weikang , Han, Mingyue , Xue, Li , Dong, Heng , Yang, Changcai , Chen, Riqing et al. GPI-Net: Gestalt-Guided Parallel Interaction Network via Orthogonal Geometric Consistency for Robust Point Cloud Registration . | arXiv , 2025 . |
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Correspondence learning aims to identify correct correspondences from the initial correspondence set and estimate camera pose between a pair of images. At present, Transformer-based methods have make notable progress in the correspondence learning task due to their powerful non-local information modeling capabilities. However, these methods seem to neglect local structures during feature aggregation from all query-key pairs, resulting in computational inefficiency and inaccurate correspondence identification. To address this issue, we propose a novel Context-aware Local and Global interaction Transformer (CLGFormer), a lightweight Transformer-based module with dual-branches that address local and global context perception in attention mechanisms. CLGFormer explores the relationship between neighborhood consistency observed in correspondences and context-aware weights appearing in vanilla attention and introduces an attention-style convolution operator. On top of that, CLGFormer also incorporates a cascaded operation that splits full features into multiple subsets and then feeds to the attention heads, which not only reduces computational costs but also enhances attention diversity. At last, we also introduce a feature recombination operate with high jointness and a lightweight channel attention module. The culmination of our efforts is the Context-aware Local and Global interaction Network (CLG-Net), which accurately estimates camera pose and identifies inliers. Through rigorous experiments, we demonstrate that our CLG-Net network outperforms existing state-of-the-art methods while exhibiting robust generalization capabilities across various scenarios. Code will be available at https://github.com/guobaoxiao/CLG.
Keyword ：
Accuracy Accuracy Circuits and systems Circuits and systems Convolution Convolution correspondence learning correspondence learning Deep learning Deep learning Feature extraction Feature extraction Feature matching Feature matching Learning systems Learning systems lightweight transformer lightweight transformer Transformers Transformers
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| GB/T 7714 | Shen, Minjun , Xiao, Guobao , Yang, Changcai et al. CLG-Net: Rethinking Local and Global Perception in Lightweight Two-View Correspondence Learning [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (1) : 207-218 . |
| MLA | Shen, Minjun et al. "CLG-Net: Rethinking Local and Global Perception in Lightweight Two-View Correspondence Learning" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35 . 1 (2025) : 207-218 . |
| APA | Shen, Minjun , Xiao, Guobao , Yang, Changcai , Guo, Junwen , Zhu, Lei . CLG-Net: Rethinking Local and Global Perception in Lightweight Two-View Correspondence Learning . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (1) , 207-218 . |
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Correspondence pruning aims to remove false correspondences (outliers) from an initial putative correspondence set. This process holds significant importance and serves as a fundamental step in various applications within the fields of remote sensing and photogrammetry. The presence of noise, illumination changes, and small overlaps in remote sensing images frequently result in a substantial number of outliers within the initial set, thereby rendering the correspondence pruning notably challenging. Although the spatial consensus of correspondences has been widely used to determine the correctness of each correspondence, achieving uniform consensus can be challenging due to the uneven distribution of correspondences. Existing works have mainly focused on either local or global consensus, with a very small perspective or large perspective, respectively. They often ignore the moderate perspective between local and global consensus, called group consensus, which serves as a buffering organization from local to global consensus, hence leading to insufficient correspondence consensus aggregation. To address this issue, we propose a multi-granularity consensus network (MGCNet) to achieve consensus across regions of different scales, which leverages local, group, and global consensus to accomplish robust and accurate correspondence pruning. Specifically, we introduce a GroupGCN module that randomly divides the initial correspondences into several groups and then focuses on group consensus and acts as a buffer organization from local to global consensus. Additionally, we propose a Multi-level Local Feature Aggregation Module that adapts to the size of the local neighborhood to capture local consensus and a Multi-order Global Feature Module to enhance the richness of the global consensus. Experimental results demonstrate that MGCNet outperforms state-of-the-art methods on various tasks, highlighting the superiority and great generalization of our method. In particular, we achieve 3.95% and 8.5% mAP5 degrees improvement without RANSAC on the YFCC100M dataset in known and unknown scenes for pose estimation, compared to the second-best models (MSA-LFC and CLNet). Source code: https://github.com/1211193023/MGCNet.
Keyword ：
Correspondence pruning Correspondence pruning Group consensus Group consensus Image matching Image matching Image registration Image registration Multi-granularity consensus Multi-granularity consensus Remote sensing image Remote sensing image
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| GB/T 7714 | Zhuang, Fengyuan , Liu, Yizhang , Li, Xiaojie et al. MGCNet: Multi-granularity consensus network for remote sensing image correspondence pruning [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2025 , 219 : 38-51 . |
| MLA | Zhuang, Fengyuan et al. "MGCNet: Multi-granularity consensus network for remote sensing image correspondence pruning" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 219 (2025) : 38-51 . |
| APA | Zhuang, Fengyuan , Liu, Yizhang , Li, Xiaojie , Zhou, Ji , Chen, Riqing , Wei, Lifang et al. MGCNet: Multi-granularity consensus network for remote sensing image correspondence pruning . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2025 , 219 , 38-51 . |
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. Pomelo quality grading helps to control the quality of pomelo production and promotes a standardized production. The realization of automated pomelo quality grading relies on the acquisition of several phenotypic parameters, with pomelo volume being a crucial one. Fruit phenotypic features, such as projected area, perimeter, radial distance, and the maximum radius of the tangent circle, are often used to estimate fruit volume through image processing. To enhance the accuracy of volume estimation, this article introduces a pomelo volume estimation model that relies on image features. This model is based on an analysis of the fruits' symmetry axis and combines various features, including the projected area, perimeter, radial distance, and the maximum radius of the tangent circle. In this study, we collected a dataset of 3,200 pomelo images and compared the prediction results under different parameter combinations. After incorporating the parameters associated with the symmetry axis, the root mean square error of volume prediction was 159.82. The coefficient of determination was 0.84, with a mean absolute error of 142.67 and a mean absolute percentage error of 0.23. Notably, the coefficient of determination was improved by 0.26 compared to the results obtained without considering the symmetry axis features. The prediction error falls within the acceptable range, meeting the requirements of practical application. Overall, the proposed method provides a valuable technical reference for pomelo quality grading.
Keyword ：
Axis of symmetry Axis of symmetry Feature parameters Feature parameters Machine learning Machine learning Volume prediction Volume prediction
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| GB/T 7714 | Lin, Yaohai , Yang, Enqi , Chen, Wei et al. VOLUME PREDICTION METHOD FOR POMELO BASED ON SYMMETRY AXIAL [J]. | APPLIED ENGINEERING IN AGRICULTURE , 2025 , 41 (2) : 195-204 . |
| MLA | Lin, Yaohai et al. "VOLUME PREDICTION METHOD FOR POMELO BASED ON SYMMETRY AXIAL" . | APPLIED ENGINEERING IN AGRICULTURE 41 . 2 (2025) : 195-204 . |
| APA | Lin, Yaohai , Yang, Enqi , Chen, Wei , Lin, Chuancong , Chen, Riqing , Yang, Changcai et al. VOLUME PREDICTION METHOD FOR POMELO BASED ON SYMMETRY AXIAL . | APPLIED ENGINEERING IN AGRICULTURE , 2025 , 41 (2) , 195-204 . |
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Background As one of the most important cereal crops, wheat (Triticum aestivum L.) production and grain quality are essential to many nations in the world. Early developmental phases such as seed germination and seedling establishment are key to wheat's growth and development as they impact directly on a crop's early performance and yield potential. Hence, it is critical to develop varieties with favorable early growth characteristics under various growing conditions.Results Here, we present SeedGerm-VIG, an automated and comprehensive pipeline developed for assessing seed vigor in wheat and other cereal crops. Building on the SeedGerm system, we integrated multiple deep learning models (i.e., YOLOv8x-Germ and optimized U-Net) and computer vision algorithms into the automated seed-level analysis pipeline to identify key germination phases and measure seed-, root-, and seedling-level phenotypic traits. Then, by using a time-series directed graph, we not only tracked root tips to reliably measure root emergence during the germination procedure (seed-lot R2 = 84.1%) but also established a new approach to examine speed and uniformity of seed germination. These resulted in the establishment of a vigor scoring matrix, through which 21 commercial genotypes' (n = 494 randomly sampled seeds, with over 29,500 seed-level images) vigor scores were summarized and evaluated at key phases such as protrusion, radicle emergence, and chloroplast biogenesis. These measures largely matched with manual assessment based on the International Seed Testing Association (ISTA) guidelines. Finally, we also demonstrated that the SeedGerm-VIG pipeline could be used to assess seed vigor for other cereal crops, including rice (n = 120 seeds) and barley (n = 240 seeds), reproducibly.Conclusions In conclusion, we believe that our work demonstrates a valuable step forward to enable a broader plant and crop research community to examine seed vigor and vigor-related phenotypic features in an automated manner, facilitating effective and scalable plant selection and relevant seed science research for crop improvement.
Keyword ：
dynamic trait analysis dynamic trait analysis germination germination seed vigor seed vigor vision-based deep learning vision-based deep learning wheat wheat
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| GB/T 7714 | Dai, Jie , Wen, Zhenjie , Ali, Mujahid et al. SeedGerm-VIG: an open and comprehensive pipeline to quantify seed vigor in wheat and other cereal crops using deep learning-powered dynamic phenotypic analysis [J]. | GIGASCIENCE , 2025 , 14 . |
| MLA | Dai, Jie et al. "SeedGerm-VIG: an open and comprehensive pipeline to quantify seed vigor in wheat and other cereal crops using deep learning-powered dynamic phenotypic analysis" . | GIGASCIENCE 14 (2025) . |
| APA | Dai, Jie , Wen, Zhenjie , Ali, Mujahid , Huang, Jinlong , Liu, Shuchen , Zhao, Jianhua et al. SeedGerm-VIG: an open and comprehensive pipeline to quantify seed vigor in wheat and other cereal crops using deep learning-powered dynamic phenotypic analysis . | GIGASCIENCE , 2025 , 14 . |
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