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学者姓名：陈昊
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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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With the development of smart agriculture, fruit picking robots have attracted widespread attention as one of the key technologies to improve agricultural productivity. Visual perception technology plays a crucial role in fruit picking robots, involving precise fruit identification, localization, and grasping operations. This paper reviews the research progress in the visual perception technology for fruit picking robots, focusing on key technologies such as camera types used in picking robots, object detection techniques, picking point recognition and localization, active vision, and visual servoing. First, the paper introduces the application characteristics and selection criteria of different camera types in the fruit picking process. Then, it analyzes how object detection techniques help robots accurately recognize fruits and achieve efficient fruit classification. Next, it discusses the picking point recognition and localization technologies, including vision-based 3D reconstruction and depth sensing methods. Subsequently, it elaborates on the adaptability of active vision technology in dynamic environments and how visual servoing technology achieves precise localization. Additionally, the review explores robot mobility perception technologies, focusing on V-SLAM, mobile path planning, and task scheduling. These technologies enhance harvesting efficiency across the entire orchard and facilitate better collaboration among multiple robots. Finally, the paper summarizes the challenges in current research and the future development trends, aiming to provide references for the optimization and promotion of fruit picking robot technology.
Keyword ：
agricultural robotics agricultural robotics intelligent fruit harvesting robots intelligent fruit harvesting robots object detection object detection visual perception visual perception visual servoing visual servoing V-SLAM V-SLAM
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| GB/T 7714 | Huang, Yikun , Xu, Shuyan , Chen, Hao et al. A review of visual perception technology for intelligent fruit harvesting robots [J]. | FRONTIERS IN PLANT SCIENCE , 2025 , 16 . |
| MLA | Huang, Yikun et al. "A review of visual perception technology for intelligent fruit harvesting robots" . | FRONTIERS IN PLANT SCIENCE 16 (2025) . |
| APA | Huang, Yikun , Xu, Shuyan , Chen, Hao , Li, Gang , Dong, Heng , Yu, Jie et al. A review of visual perception technology for intelligent fruit harvesting robots . | FRONTIERS IN PLANT SCIENCE , 2025 , 16 . |
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This work presents a method for automatically detecting and recognizing test tube types in a rack. It leverages automatic segmentation, clustering, and labeling processes to eliminate the need for explicitly preparing training data. These processes are addressed by using combined global prediction and local cropping, where global prediction estimates the slot occupation states of a rack, and local cropping extracts tube pictures in the local regions of each slot for clustering and labeling. With the help of the proposed method, the robotic tube manipulation system no longer needs tailored data and explicit training in the presence of new tubes, thus achieving flexibility and efficiency. Experimental evaluations conducted with a RealSense D405 camera and the UFactory xArm Lite6 robot manipulator confirm the method's effectiveness in accurately identifying novel test tube types under real-world conditions.
Keyword ：
Deep learning Deep learning robotic manipulation robotic manipulation test tube detection test tube detection
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| GB/T 7714 | Tang, Yu , Wan, Weiwei , Chen, Hao et al. Zero-Shot Recognition of Test Tube Types by Automatically Collecting and Labeling RGB Data [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2025 , 10 (8) : 8276-8283 . |
| MLA | Tang, Yu et al. "Zero-Shot Recognition of Test Tube Types by Automatically Collecting and Labeling RGB Data" . | IEEE ROBOTICS AND AUTOMATION LETTERS 10 . 8 (2025) : 8276-8283 . |
| APA | Tang, Yu , Wan, Weiwei , Chen, Hao , Matsushita, Masaki , Takahashi, Jun , Kotaka, Takeyuki et al. Zero-Shot Recognition of Test Tube Types by Automatically Collecting and Labeling RGB Data . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2025 , 10 (8) , 8276-8283 . |
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Purpose: Deep learning-based classroom behavior analysis provides new avenues for monitoring teaching quality in higher education. However, it faces challenges such as low detection accuracy, difficulty in recognizing small objects and handling occlusions as well as the difficulty in balancing real-time performance with accuracy. Design/methodology/approach: This paper proposes an improved YOLOv11 method for classroom state recognition, achieving precise classification and behavior detection through the integration of AFGCAttention, SPDConv and RCSOSA modules. AFGCAttention optimizes feature weight allocation through an adaptive fine-grained channel attention mechanism, SPDConv enhances the processing capabilities for small objects and low-resolution images by converting spatial information into depth information and RCSOSA reduces channel redundancy while improving spatial object attention. Findings: Experiments demonstrate that the YOLO-ASR model excels in precision, recall and mAP50. Compared to other You Only Look Once versions, it shows significantly improved detection accuracy and robustness in complex classroom environments, achieving an mAP50 of 93.8% and an mAP50-95 of 73.1%. Time-series analysis reveals dynamic changes in student behavior across teaching phases, including attention fluctuations, mobile phone use and signs of fatigue. Research limitations/implications: By analyzing student behavior across different classroom phases, patterns in mobile phone use and signs of fatigue were identified. These insights help teachers adjust their strategies, highlighting the method’s significance in monitoring teaching quality. Originality/value: This study optimizes the YOLOv11 model for classroom behavior detection by integrating effective modules to enhance performance. It offers a novel approach for quantitatively assessing teaching effectiveness, providing data support for educational reform and advancing intelligent classroom management and innovative teaching models. © 2025, Emerald Publishing Limited.
Keyword ：
Cellular telephones Cellular telephones Deep learning Deep learning Information management Information management Redundancy Redundancy Students Students Teaching Teaching
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| GB/T 7714 | Huang, Yikun , Xue, Xingsi , Chen, Hao et al. A method for classroom behavior state recognition and teaching quality monitoring [J]. | International Journal of Intelligent Computing and Cybernetics , 2025 , 18 (2) : 382-396 . |
| MLA | Huang, Yikun et al. "A method for classroom behavior state recognition and teaching quality monitoring" . | International Journal of Intelligent Computing and Cybernetics 18 . 2 (2025) : 382-396 . |
| APA | Huang, Yikun , Xue, Xingsi , Chen, Hao , Wei, Lifang , Zhang, Fuquan , Wang, Zhenyu et al. A method for classroom behavior state recognition and teaching quality monitoring . | International Journal of Intelligent Computing and Cybernetics , 2025 , 18 (2) , 382-396 . |
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Tunnel boring machines (TBM) need to replace disc cutters to ensure the efficiency of tunneling, which relies on intensive labor operations in harsh environments, highlighting the urgent need for robotic systems to substitute. Visual servoing is crucial for robots to grasp disc cutters with uncertainty. However, traditional methods face significant challenges in environments with unpredictable occlusions, contamination, and damage. Thus, we propose to develop a robust visual servo strategy for the harsh working environment in real TBMs. The major contribution of this strategy includes two parts. First, we propose an image-based desired vectors field made up of griddings of image. Second, we propose a direct and constant interaction matrix to map the camera velocity from the image-based desired vectors. These two parts increase the robustness of visual servoing for the vision-based controller, especially for working with a polluted environment and servoing uncertain states of the disc cutters. The experiments validated it is a stable, easy-employing vision controller for overcoming the difficulty in controlling cutter replacement robots in unstatic environment conditions, thus promoting the application of robotic technologies in more field situations.
Keyword ：
disc cutter replacement disc cutter replacement industrial robotics industrial robotics robotic grasping robotic grasping visual servoing visual servoing
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| GB/T 7714 | Yang, Qiang , Du, Liang , Chen, Hao et al. Visual Servoing With Grid-Based Directional Error Mapping for Robotic TBM Disc Cutter Replacement [J]. | JOURNAL OF FIELD ROBOTICS , 2025 , 42 (6) : 3003-3015 . |
| MLA | Yang, Qiang et al. "Visual Servoing With Grid-Based Directional Error Mapping for Robotic TBM Disc Cutter Replacement" . | JOURNAL OF FIELD ROBOTICS 42 . 6 (2025) : 3003-3015 . |
| APA | Yang, Qiang , Du, Liang , Chen, Hao , Bao, Sheng , Hu, Zhengtao , Yuan, Jianjun . Visual Servoing With Grid-Based Directional Error Mapping for Robotic TBM Disc Cutter Replacement . | JOURNAL OF FIELD ROBOTICS , 2025 , 42 (6) , 3003-3015 . |
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Heat stress transcription factors (HSFs) play a critical role in orchestrating cellular responses to elevated temperatures and various stress conditions. While extensively studied in model plants, the HSF gene family in Betula platyphylla remains unexplored, despite the availability of its sequenced genome. In this study, we employed bioinformatics approaches to identify 21 BpHSF genes within the Betula platyphylla genome, revealing their uneven distribution across chromosomes. These genes were categorized into three subfamilies: A, B, and C. Each was characterized by conserved protein motifs and gene structures, with notable divergence observed between subfamilies. Collinearity analysis suggested that segmental duplication events have driven the evolutionary expansion of the BpHSF gene family. Promoter region analysis identified an array of cis-acting elements linked to growth, development, hormonal regulation, and stress responses. Subcellular localization experiments confirmed the nuclear localization of BpHSFA2a, BpHSFB1a, and BpHSFC1a, consistent with in silico predictions. RNA-seq and RT-qPCR analyses revealed tissue-specific expression patterns of BpHSF genes and their dynamic responses to heat stress, with qPCR validation highlighting a significant upregulation of BpHSFA2a under high-temperature conditions. In summary, this study provided a comprehensive characterization of the HSF gene family in Betula platyphylla, laying a solid foundation for future functional studies. Particularly, BpHSFA2a emerges as a promising candidate gene for enhancing heat tolerance in Betula platyphylla, warranting further detailed investigation.
Keyword ：
Betula platyphylla Betula platyphylla gene expression gene expression heat stress transcription factor heat stress transcription factor high-temperature stress high-temperature stress subcellular localization subcellular localization
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| GB/T 7714 | Guo, Shengzhou , Chen, Hao , Wu, Hongwei et al. Genome-Wide Characterization of the Heat Shock Transcription Factor Gene Family in Betula platyphylla Reveals Promising Candidates for Heat Tolerance [J]. | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES , 2025 , 26 (1) . |
| MLA | Guo, Shengzhou et al. "Genome-Wide Characterization of the Heat Shock Transcription Factor Gene Family in Betula platyphylla Reveals Promising Candidates for Heat Tolerance" . | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 26 . 1 (2025) . |
| APA | Guo, Shengzhou , Chen, Hao , Wu, Hongwei , Xu, Zuyuan , Yang, Hao , Lin, Qinmin et al. Genome-Wide Characterization of the Heat Shock Transcription Factor Gene Family in Betula platyphylla Reveals Promising Candidates for Heat Tolerance . | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES , 2025 , 26 (1) . |
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