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61
Infrared Ship Detection in Complex Nearshore Scenes Based on Improved YOLOv5s
Published 2025-06-01“…Accurate ship identification and classification are central to this objective, underscoring the critical importance of ship detection technology. However, compared to open-sea surface, dense vessel distributions and complex backgrounds in nearshore areas substantially limit detection efficacy. …”
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62
Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background
Published 2025-07-01“…Experimental results show that the proposed YOLO-SSM algorithm has obvious advantages in accuracy and model complexity and can provide reliable theoretical support for efficient and accurate detection and identification of tea leaf diseases in natural scenes.…”
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63
Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments
Published 2025-07-01“…Previous approaches often struggle with complex agricultural environments where lighting conditions, pig postures, and crowding levels create challenging detection scenarios. …”
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64
YOLO-RDM: Innovative Detection Methods for Eggplants and Stems in Complex Natural Environment
Published 2025-01-01“…To achieve rapid identification of eggplants and stems in complex environments and enhance overall accuracy, this study establishes a database for eggplants and stems and proposes an efficient detection model based on the YOLOv8n network. …”
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65
Investigation towards nanomechanical sensor array for real-time detection of complex gases
Published 2025-03-01Get full text
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66
An enhanced lightweight model for apple leaf disease detection in complex orchard environments
Published 2025-03-01“…However, in complex natural environments, factors such as light variations, shading from branches and leaves, and overlapping disease spots often result in reduced accuracy in detecting apple diseases. …”
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67
Binocular Vision-Based Target Detection Algorithm
Published 2025-01-01“…In this study, the Feffol network model is proposed to achieve excellent performance in complex detection tasks. In the feature extraction stage, the Feffol network model carefully chooses Efficient-v2 as the backbone network structure, which can provide a solid foundation for subsequent detection by taking advantage of its efficient feature extraction capability. …”
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68
GLS-YOLO: A Lightweight Tea Bud Detection Model in Complex Scenarios
Published 2024-12-01“…To address the demand for high-precision yet lightweight tea bud detection, this study proposes the GLS-YOLO detection model, based on YOLOv8. …”
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69
A review: Multi-Objective Algorithm for Community Detection in Complex Social Networks
Published 2025-02-01“…Recently, research on multi-objective optimization algorithms for community detection in complex networks has grown considerably. …”
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70
Application of GPR Underground Pipeline Detection Technology in Urban Complex Geological Environments
Published 2022-01-01“…This research applies bottom detection radar to urban underground pipeline detection technology under complex conditions for the first time, innovatively uses the action mechanism of bottom detection radar, integrates its high precision and high efficiency into underground pipeline detection technology, and ensures the effectiveness of the detection work.…”
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71
Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach
Published 2024-12-01“…The Efficient Channel Attention (ECA) mechanism was integrated into the YOLOv11 model to enhance the model’s ability to focus on key features, thereby significantly improving detection accuracy. …”
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72
Enhancing wind turbine blade damage detection with YOLO-Wind
Published 2025-05-01“…Furthermore, a newly added P2 detection layer enhances multi-scale defect recognition in complex environments. …”
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73
Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images
Published 2024-11-01“…We propose an efficient small-object YOLO detection model (ESOD-YOLO) based on YOLOv8n for Unmanned Aerial Vehicle (UAV) object detection. …”
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74
Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs
Published 2024-07-01“…Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which in turn affects the effectiveness of defect detection. …”
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An FPGA Prototype for Parkinson’s Disease Detection Using Machine Learning on Voice Signal
Published 2025-01-01“…This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. …”
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77
EfficientTransformer: A Dynamic Anomaly Detection Model for Industrial Control Networks
Published 2025-01-01“…This reduces computational complexity and enables efficient parallel learning. …”
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78
FoT: an efficient transformer framework for real-time small object detection in football videos
Published 2025-08-01“…Abstract Football videos are playing an increasingly important role in event analysis and tactical evaluation within computer vision. Traditional object detection methods, relying on region proposals and anchor generation, struggle to balance real-time performance and accuracy in complex scenarios such as multi-view, motion blur, and small object recognition. …”
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79
SCoralDet: Efficient real-time underwater soft coral detection with YOLO
Published 2025-03-01“…In recent years, climate change and marine pollution have significantly degraded coral reefs, highlighting the urgent need for automated coral detection to monitor marine ecosystems. However, underwater coral detection presents unique challenges, including low image contrast, complex coral structures, and dense coral growth, which limit the effectiveness of general object detection algorithms. …”
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SD-YOLO: A Robust and Efficient Object Detector for Aerial Image Detection
Published 2025-01-01“…Aerial image detection remains challenging due to the varying scales of objects and complex backgrounds. …”
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