Yingchao Zhang

Yingchao Zhang

yingchao.zhang@my.cityu.edu.hk

I am a second year PhD candidate in the Department of Systems Engineering at the City University of Hong Kong, and my current research interest is non-destructive testing in industry.

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Road surface defects detection based on imu sensor

Published in IEEE Sensors Journal, 2021

This paper proposes a fully connected neural network (FCNN) based on the inertial measurement unit (IMU), because of the characteristics of IMU with fewer data but carrying more information. Different signals (acceleration, velocity, and Euler angle) were processed by the data processing method proposed and made into databases with various features.

Recommended citation: Zhang, Y., Ma, Z., Song, X., Wu, J., Liu, S., Chen, X., & Guo, X. (2021). Road surface defects detection based on imu sensor. IEEE Sensors Journal, 22(3), 2711-2721.
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Road damage detection using UAV images based on multi-level attention mechanism

Published in Automation in Construction, 2022

This study uses the unmanned aerial vehicle (UAV) road damage database and describes a multi-level attention mechanism called Multi-level Attention Block (MLAB) to strengthen the utilization of essential features by the You Only Look Once version 3 (YOLO v3). Adding MLAB between the backbone and feature fusion parts effectively increases the mAP value of the proposed network to 68.75%, while the accuracy of the original network is only 61.09%. The network is able to detect longitudinal cracks, transverse cracks, repairs, and potholes with high accuracy, and significantly improves the accuracy of alligator cracks and oblique cracks.

Recommended citation: Zhang, Y., Zuo, Z., Xu, X., Wu, J., Zhu, J., Zhang, H., ... & Tian, Y. (2022). Road damage detection using UAV images based on multi-level attention mechanism. Automation in construction, 144, 104613.
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Network for robust and high-accuracy pavement crack segmentation

Published in Automation in Construction, 2024

this study proposes a pavement crack segmentation algorithm called MixCrackNet. MixCrackNet leverages deformable convolution, weighted loss functions, an efficient multi-scale attention module, and the Mix Structure to identify pavement cracks. Three datasets were used to train and validate the effectiveness of MixCrackNet. By comparing with classical semantic segmentation networks, the results demonstrate that MixCrackNet outperforms all the other models in crack segmentation. Furthermore, MixCrackNet not only exhibits exceptional performance across all three datasets, but also achieves decent results in untrained dataset. These results indicate that MixCrackNet is not only highly accurate but also robust, thereby promoting the application of semantic crack segmentation technology in pavement condition detection.

Recommended citation: Zhang, Y., & Liu, C. (2024). Network for robust and high-accuracy pavement crack segmentation. Automation in Construction, 162, 105375.
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Paper Title Tits 2024

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Undergraduate course, Department of Systems Engineering, City University of Hong Kong, 2024