Showing 921 - 940 results of 3,615 for search 'complex detection (coefficient OR (efficient OR efficiency))', query time: 0.22s Refine Results
  1. 921

    Off-target sequence variations driven by the intrinsic properties of the Cas-sgRNA-DNA complex in genome editing. by Celine Kurniawan, Takeshi Itoh

    Published 2025-01-01
    “…Computational approaches are anticipated to streamline the detection of off-target mutations; however, the performance of current prediction tools is limited, likely owing to insufficient knowledge of off-target mutation characteristics. …”
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    Article
  2. 922

    Deep Reinforcement Learning-Based Motion Control Optimization for Defect Detection System by Yuhuan Cai, Liye Zhao, Xingyu Chen, Zhenjun Li

    Published 2025-04-01
    “…For practical implementation and validation, a PMSM simulation model is constructed in MATLAB/Simulink, serving as an interactive training platform for the DRL agent and facilitating efficient, robust training. The simulation results validate the effectiveness and superiority of the proposed optimization strategy, demonstrating its applicability and potential for precise and robust control in complex nonlinear defect detection systems.…”
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  3. 923

    Target Detection and Image Enhancement for Underwater Environment: Research on Improving YOLOv7 by Yang Luo, Wen Feng

    Published 2025-01-01
    “…Aiming at the common low accuracy and efficiency problems in underwater target detection, this paper designs an innovative algorithm based on the YOLOv7 framework. …”
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    Article
  4. 924

    Dynamic response and discrimination of gaseous sarin using a boron‐difluoride complex film‐based fluorescence sensor by Zhijie Zhou, Lei Zhang, Lingya Peng, Yingjie Li, Xiaolin Zhu, Yidi Wu, Zebiao Qiu, Gang He, Molin Qin, Haonan Peng, Yu Fang

    Published 2024-12-01
    “…Abstract This study presents a novel boron‐difluoride complex‐based fluorescent nanofilm sensor capable of detecting sarin vapors in the environment by reporting an output fluorescence signal. …”
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    Article
  5. 925

    Subsea Nodule Recognition and Deployment Detection Method Based on Improved YOLOv8s by Jixin Li, Junchao Li, Bin Su, Yuxin Cui

    Published 2025-01-01
    “…An improved small-target detection model based on YOLOv8s is proposed to address the challenges associated with deep-sea polymetallic nodule detection, such as complex target shapes, small sizes, and strong environmental interference. …”
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    Article
  6. 926

    YOLOv-MA: A High-Precision Foreign Object Detection Algorithm for Rice by Jiahui Wang, Mengdie Jiang, Tauseef Abbas, Hao Chen, Yuying Jiang

    Published 2025-06-01
    “…Rice plays a crucial role in global agricultural production, but various foreign objects often mix in during its processing. To efficiently and accurately detect small foreign objects in the rice processing pipeline, ensuring food quality and consumer safety, this study innovatively proposes a YOLOv-MA-based foreign object detection algorithm for rice, leveraging deep learning techniques. …”
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    Article
  7. 927

    An improved method of AUD-YOLO for surface damage detection of wind turbine blades by Li Zou, Anqi Chen, Xinhua Yang, Yibo Sun

    Published 2025-02-01
    “…Abstract The detection of wind turbine blades (WTBs) damage is crucial for improving power generation efficiency and extending the lifespan of turbines. …”
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    Article
  8. 928

    Inline Wear Detection in High-Speed Progressive Dies Using Photometric Stereo by Moske Jonas, Kutlu Hasan, Steinmeier Adrian, Groenewold Phil, Santos Pedro, Kuijper Arjan, Weinmann Andreas, Groche Peter

    Published 2025-01-01
    “…The progressive forming of complex components, including blanking, deep-drawing, and bending, represents an economically efficient sheet metal processing method. …”
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    Article
  9. 929

    A Rapid Concrete Crack Detection Method Based on Improved YOLOv8 by Yongzhen Wang, Jiacong He

    Published 2025-01-01
    “…An improved YOLOv8 (You Only Look Once version 8) model is proposed to tackle the challenges of low detection accuracy and slow speed resulting from the complex background and shape diversity of concrete cracks. …”
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    Article
  10. 930

    FUR-DETR: A Lightweight Detection Model for Fixed-Wing UAV Recovery by Yu Yao, Jun Wu, Yisheng Hao, Zhen Huang, Zixuan Yin, Jiajing Xu, Honglin Chen, Jiahua Pi

    Published 2025-05-01
    “…However, the existing RT-DETR algorithm is limited by single-path feature extraction, a simplified fusion mechanism, and high-frequency information loss, which makes it difficult to balance detection accuracy and computational efficiency. Therefore, this paper proposes a lightweight visual detection model based on transformer architecture to further optimize computational efficiency. …”
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    Article
  11. 931
  12. 932

    Sports video temporal action detection technology based on an improved MSST algorithm by Lai Lixin, Fang Yu

    Published 2025-07-01
    “…Sports videos contain a large number of irrelevant backgrounds and static frames, which affect the efficiency and accuracy of temporal action detection. …”
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    Article
  13. 933

    BDK-YOLOv8: An Enhanced Algorithm for UAV Infrared Image Object Detection by Nan Xiao, Xianggong Hong, Zixuan Zheng

    Published 2024-01-01
    “…Finally, channel pruning is applied to reduce model parameters and computational complexity, improving detection efficiency.Experimental results show that the proposed algorithm achieves 94.3% mAP50 on the HIT-UAV dataset, a 1.6% improvement over the original YOLOv8n, with a 3% increase in recall. …”
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  14. 934

    HGLFNet: Hybrid Global Semantic and Local Detail Feature Network for Lane Detection by Lei Ding, Chunhui Tang, Yi Fang

    Published 2025-01-01
    “…The experimental results comprehensively demonstrate that HGLFNet surpasses the existing state-of-the-art techniques in both accuracy and efficiency, providing a novel and effective solution for lane detection in complex scenarios and showing significant potential in practical applications.…”
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  15. 935

    Double-layer membrane framework-based gold microelectrode for determination of natural labile copper in complex water environments by Xinyue Hu, Haitao Han, Shanshan Wang, Dawei Pan

    Published 2025-03-01
    “…However, the determination of low concentration labile Cu (CuLabile) in complex water environments remains a huge challenge. …”
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  16. 936

    Lightweight Small Target Detection Algorithm Based on YOLOv8 Network Improvement by Xiaoyi Hao, Ting Li

    Published 2025-01-01
    “…The modules have been designed to optimise feature extraction and improve model efficiency. The paper also discusses the challenges associated with low accuracy in small target detection and high model complexity in UAV applications. …”
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    Article
  17. 937

    Quantum Edge Detection and Convolution Using Paired Transform-Based Image Representation by Artyom Grigoryan, Alexis Gomez, Sos Agaian, Karen Panetta

    Published 2025-03-01
    “…Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. …”
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  18. 938

    Application of the YOLOv11-seg algorithm for AI-based landslide detection and recognition by Luhao He, Yongzhang Zhou, Lei Liu, Yuqing Zhang, Jianhua Ma

    Published 2025-04-01
    “…Compared with traditional methods, YOLOv11-seg performs better in detecting complex boundaries and handling occlusion, demonstrating superior detection accuracy and segmentation quality. …”
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    Article
  19. 939

    Pear Fruit Detection Model in Natural Environment Based on Lightweight Transformer Architecture by Zheng Huang, Xiuhua Zhang, Hongsen Wang, Huajie Wei, Yi Zhang, Guihong Zhou

    Published 2024-12-01
    “…This model provides technical support for Xinli No. 7 fruit detection and model deployment in complex environments.…”
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  20. 940

    YOLO-MES: An Effective Lightweight Underwater Garbage Detection Scheme for Marine Ecosystems by Chengxu Huang, Wenyuan Zhang, Beitian Zheng, Jiahao Li, Bochen Xie, Ruisi Nan, Zongming Tan, Baohua Tan, Neal N. Xiong

    Published 2025-01-01
    “…Experimental results indicate that YOLO-MES achieves 95.8% accuracy on the dataset, while reducing model size and computational complexity by 64% and 67%, respectively. Compared to existing mainstream detection algorithms, YOLO-MES offers significant advantages in lightweight design and computational efficiency, providing a practical and deployable solution for underwater target detection on mobile devices.…”
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