A NanoDet Model with Adaptively Weighted Loss for Real-time Railroad Inspection



Published Oct 26, 2023
Jiawei Guo Sen Zhang Yu Qian Yi Wang


Monitoring the railroad’s components is crucial to maintaining the safety of railway operations. This article proposes a novel, compact computational vision system that works on edge devices, designed to provide precise, instantaneous assessments of rail tracks. This model reconfigures the teacher-student guidance system inherent in NanoDet by incorporating an innovative adaptively weighted loss (AWL) in the learning phase. The AWL assesses the caliber of the teacher and student models, establishes the weightage of the student's loss, and dynamically adjusts their loss contributions, directing the learning procedure towards effective knowledge transfer and direction. In comparison with cutting-edge models, our AWL-NanoDet boasts a minuscule model size of less than 2 MB and a computational expense of 1.52 G FLOPs, delivering a processing time of less than 14 ms per frame (evaluated on Nvidia’s AGX Orin). Compared to the original NanoDet, it also significantly enhances the model's accuracy by nearly 6.2%, facilitating extremely precise, instantaneous recognition of rail track elements.

How to Cite

Guo, J., Zhang, S., Qian, Y., & Wang, Y. (2023). A NanoDet Model with Adaptively Weighted Loss for Real-time Railroad Inspection . Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3498
Abstract 196 | PDF Downloads 144



Deep Learning, Rail inspection, Computer vision, Edge Computing

RangiLyu., “NanoDet ,” https://github.com/RangiLyu/nanodet.

FRA, “Train accidents by cause form (form FRA F 6180.54).” https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/Query/inccaus.aspx.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, pp. 2278–2324, 1998.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun ACM, vol. 60, pp. 84–90, 2017.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in IEEE conference on computer vision and pattern recognition, 2014, pp. 580–587.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.

H. Law and J. Deng, “Cornernet: Detecting objects as paired keypoints,” in European conference on computer vision, 2018, pp. 734–750.

Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” in IEEE/CVF international conference on computer visio, 2019, pp. 9627–9636.

A. G. Howard et al., “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint, 2017.

X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” in IEEE conference on computer vision and pattern recognition, 2018, pp. 6848–6856.

G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint, 2015.

C. H. Nguyen, T. C. Nguyen, T. N. Tang, and N. L. Phan, “Improving object detection by label assignment distillation,” in IEEE/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 1005–1014.

D. Dais, I. E. Bal, E. Smyrou, and V. Sarhosis, “Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning,” Autom Constr, vol. 125, p. 103606, 2021.

J. Zhang, S. Qian, and C. Tan, “Automated bridge crack detection method based on lightweight vision models,” Complex & Intelligent Systems, pp. 1–14, 2022.

Y.-J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, and O. Büyüköztürk, “Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types,” Computer-Aided Civil and Infrastructure Engineering, vol. 33, pp. 731–747, 2018.

F. Guo, Y. Qian, and Y. Shi, “Real-time railroad track components inspection based on the improved YOLOv4 framework,” Autom Constr, vol. 125, p. 103596, 2021.

F. Guo, Y. Qian, Y. Wu, Z. Leng, and H. Yu, “Automatic railroad track components inspection using real-time instance segmentation,” Computer-Aided Civil and Infrastructure Engineering, vol. 36, pp. 362–377, 2021.

S. Li, X. Zhao, and G. Zhou, “Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network,” Computer-Aided Civil and Infrastructure Engineering, vol. 34, pp. 616–634, 2019.


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