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HongYing Qin

As seen in: Nature, Wiley Online Library
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Articles

Image segmentation of track bolts based on improved YOLO model

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By Zhe Liu, HongYing Qin, Chang Wen, Kai Xie, Wei Zhang, Nianen Jiang
| Nature Verified
Abstract Railway track bolt detection is critical for high-speed railway safety maintenance, yet existing methods often suffer from high false detection rates and poor robustness under complex illumination, heavy contamination, and varying scale conditions, limiting their practical deployment. To address these issues, this paper proposes an improved YOLO-based segmentation algorithm for railway track bolt images.
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Improved Target Detection in Unmanned Aerial Vehicles Search and Rescue via V‐Circular Trajectory Optimization

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By Kaiyu Wang, Zhanzhan Shi, HongYing Qin
| Wiley Online Library Verified
Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References , , , et al. 2023. “YOLO-Based UAV Technology: A Review of the Research and Its Applications.” Drones 7, no. 3: 190. , , , , and . 2022. “The Influence of Flight Height and Overlap on UAV Imagery over Featureless Surfaces and Constructing Formulas Predicting the Geometrical Accuracy.” NRIAG Journal of Astronomy and Geophysics 11, no. 1: 210–223.
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