Railway Inspection Group

M. Li, B. Peng and D. Zhai, "Latent Space Segmentation Model for Visual Surface Defect Inspection," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-11, 2024, Art no. 5029111, doi: 10.1109/TIM.2024.3446650. [web]
M. Li, B. Peng, J. Liu and D. Zhai, "RBNet: An Ultrafast Rendering-Based Architecture for Railway Defect Segmentation," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-8, 2023, Art no. 2512808, doi: 10.1109/TIM.2023.3269107. [web]
M. Li, D. Zhai, D. Yang and L. Xu, "BVTracker: Multivehicle Tracking Based on Behavioral-Visual Features," in IEEE Sensors Journal, vol. 23, no. 11, pp. 11815-11824, 1 June1, 2023, doi: 10.1109/JSEN.2023.3265659. [web]
D. Zhai, X. Zhang, X. Li, X. Xing, Y. Zhou, and C. Ma, "Object detection methods on compressed domain videos: An overview, comparative analysis, and new directions," Measurement, vol. 207, p. 112371, 2023, doi: 10.1016/j.measurement.2022.112371 . [web]
D. Zhai, D. Yang, J. Chen, Z. Luo, M. Yu, and Z. Zhou, "Model for the cooperative obstacle-avoidance of the automated vehicle swarm in a connected vehicles environment," IET Intelligent Transport Systems, vol. 17, no. 6, pp. 1137-1151, 2023, doi: 10.1049/itr2.12359. [web]
X. Zhang, Y. Zhou, Z. Lu, D. Zhai, H. Luo, T. Li, and Y. Li, "Multi-level Graph Neural Network with Sparsity Pooling for Recognizing Parkinson's Disease," IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, doi: 10.1109/TNSRE.2023.3330643. [web]
D. Zhai, X. Lai, J. Meng, G. Liu, J. Wu and S. Xiao, "The Hybrid Suspension System for Middle-to-Low-Speed Maglev Trains Considering the Prevention of Firm Absorption," in IEEE Transactions on Transportation Electrification, vol. 8, no. 1, pp. 1482-1492, March 2022, doi: 10.1109/TTE.2021.3109166. [web]
D. Zhai, B. Hu, X. Gong, H. Zou, and J. Luo, “ASS-GAN: Asymmetric semi-supervised GAN for breast ultrasound image segmentation,” Neurocomputing, vol. 493, pp. 204–216, Jul. 2022, doi: 10.1016/j.neucom.2022.04.021. [web]
X. Zhang, D. Zhai, T. Li, Y. Zhou, and Y. Lin, "Image Inpainting Based on Deep Learning: A Review," Information Fusion, 2022, doi: 10.1016/j.inffus.2022.08.033. [web]