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Yunnan University

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Research library168linked papers
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Selected work

Representative Papers

RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images

Aug 30, 2022Applied Sciences

To address critical challenges in satellite remote sensing imagery—namely, low detection accuracy, poor small-object recognition, and severe foreground-background class imbalance—this paper proposes RS-YOLOX. Methodologically, it integrates the ECA (Efficient Channel Attention) mechanism into the YOLOX backbone to enhance channel-wise feature responsiveness; adopts Adaptive Spatial Feature Fusion (ASFF) for adaptive multi-scale feature aggregation; employs Varifocal Loss to mitigate class imbalance and improve hard-example learning; and incorporates Slice-Assisted Hyper-Inference (SAHI) to boost small-object recall. Evaluated on three benchmark remote sensing datasets—DOTA-v1.5, TGRS-HRRSD, and RSOD—RS-YOLOX achieves state-of-the-art (SOTA) performance across all, with particularly notable gains in small-object detection and dense-scene scenarios. These results validate both the effectiveness and generalizability of the proposed framework.

28 citationsRead paper

Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

Nov 28, 2023Applied Sciences

To address low detection accuracy in identifying foreign objects (e.g., balloons, kites, bird nests) on high-voltage transmission lines—caused by severe occlusion, large scale variations, and complex backgrounds—this paper proposes a lightweight and efficient detection method based on an improved YOLOv8m architecture. The method introduces three key innovations: (1) a Global Attention Mechanism (GAM) to enhance perception of occluded targets; (2) SPPCSPC replacing SPPF to improve multi-scale feature fusion efficiency; and (3) a Focal-EIoU loss function to mitigate positive–negative sample imbalance. Evaluated on a real-world dataset collected from Yunnan Power Grid, the proposed model achieves +2.7% mAP₀.₅, +4.0% mAP₀.₅:₀.₉₅, and +6.0% recall over the baseline. These improvements significantly enhance robustness and practicality for detecting small and occluded foreign objects under challenging field conditions.

17 citations1 influentialRead paper

An Appearance Defect Detection Method for Cigarettes Based on C-CenterNet

Jul 12, 2022Electronics

To address the poor adaptability, low localization accuracy, and insufficient classification performance in detecting surface defects (e.g., dents, deformations, stains) on automated cigarette production lines, this paper proposes C-CenterNet—a novel end-to-end object detection framework. Our method innovatively integrates the Convolutional Block Attention Module (CBAM), deformable convolutions, and the ACON adaptive activation function into the CenterNet architecture, leveraging a ResNet50 backbone with Feature Pyramid Network (FPN) to enhance robust center-point localization and attribute regression for multi-scale, fine-grained defects. Evaluated on a real-world industrial dataset, C-CenterNet achieves a mean Average Precision (mAP) of 95.01%, outperforming the baseline CenterNet by 6.14 percentage points. The model maintains high detection accuracy while satisfying real-time inference requirements, making it suitable for practical deployment in industrial environments.

17 citations1 influentialRead paper

Improved YOLOv5s model for key components detection of power transmission lines.

Feb 20, 2023Mathematical biosciences and engineering : MBE

To address the low detection accuracy of small critical components (e.g., insulators, hardware) in complex backgrounds during intelligent inspection of high-voltage transmission lines, this paper proposes a lightweight and efficient YOLOv5s-based detection method. Specifically, we: (i) design an IoU-optimized k-means clustering distance metric for anchor box generation; (ii) incorporate the Convolutional Block Attention Module (CBAM) to enhance multi-scale feature discrimination; and (iii) adopt Focal Loss to mitigate class imbalance among component categories. Experimental results on a custom-built transmission line dataset show that the proposed model achieves 98.1% mAP, 97.5% precision, and 94.4% recall, with an inference speed of 84.8 FPS—significantly outperforming the baseline YOLOv5s. The method thus delivers both high accuracy and real-time performance, effectively supporting practical deployment in intelligent transmission line inspection systems.

9 citations2 influentialRead paper

Exploration and Practice of Improving Programming Ability for the Undergraduates

Feb 01, 2025International Journal of Information and Education Technology

Undergraduate computer science students at universities in Western China exhibit weak programming competencies, compounded by an underdeveloped practical teaching system. Method: This study proposes a closed-loop cultivation model integrating “curriculum—platform—certification—competition,” synergizing online judge (OJ) systems, learning analytics, standardized competency assessment frameworks, and competitive programming event mechanisms to shift pedagogical evaluation from outcome-oriented to competency-based assessment. Contribution/Results: Innovatively embedding formal competency certification into the core curriculum enables dynamic alignment among instruction, hands-on training, assessment, and competition—an approach unprecedented in this context. Empirical implementation demonstrates a 23.6% increase in pass rates for programming courses and a 41.2% rise in awards at high-level competitions such as ACM-ICPC. The resulting framework constitutes a replicable, scalable paradigm for reforming practical computer science education in Western Chinese universities.

3 citationsRead paper
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