A novel high-precision lightweight concrete bridge crack image recognition method based on knowledge distillation

Abstract

To meet the demand for high-precision recognition and real-time processing when using intelligent detection methods in daily concrete bridge crack detection tasks, a high-precision lightweight crack image recognition method based on knowledge distillation is proposed. Through semisupervised learning, the information embedded in unlabeled data is used to train a lightweight network to obtain high-precision crack recognition results. First, by comparing the recognition accuracy of lightweight network under different training strategies, the effectiveness of the knowledge distillation method in improving the accuracy of lightweight network is verified. Second, a label-free knowledge distillation method for engineering detection is proposed. Finally, using MobileNetv3 as the student model, under the self-built three-classification dataset of nearly 700,000 images, the accuracy reached 99.84% through the proposed knowledge distillation method, which is very close to the teacher model ResNeXt101 accuracy (−0.08%), and MobileNetv3 compared with ResNeXt101 its model inference speed increased by more than 13 times. At the same time, the MobileNetv3 model improves the inference speed by more than 13 times and reduces the number of model parameters by more than 35 times compared to the ResNeXt101 model. The research results fully demonstrate that under the premise of having a large amount of unlabeled data, using the proposed knowledge distillation method, the recognition accuracy of the trained lightweight model has a significant advantage over the existing lightweight network training strategies.

Keywords

Concrete bridge crack high-precision lightweight network knowledge distillation transfer learning teacher model student model

Get full access to this article

View all access options for this article.

References

, et al. Automatic bridge crack detection using unmanned aerial vehicle and faster R-CNN. Constr Build Mater 2023; 362: 129659.

Zhang

Yang

Zhang

, et al. Road crack detection using deep convolutional neural network. In: 2016 IEEE international conference on image processing (ICIP), Phoenix, Arizona, 2016, pp. 3708–3712. IEEE.

Rashidi

Samali

, et al. Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm. Struct Health Monit 2022; 21: 2244–2263.

Samali

Rashidi

, et al. Vision-based concrete crack detection using a hybrid framework considering noise effect. J Build Eng 2022; 61: 105246.

Cha

Choi

Büyüköztürk

Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 2017; 32: 361–378.

Mei

Liu

, et al. Crack detection of concrete pavement with cross-entropy loss function and improved VGG16 network model. IEEE Access 2020; 8: 54564–54573.

Lin

Chen

, et al. Surface crack detection based on image stitching and transfer learning with pretrained convolutional neural network. Struct Control Health Monit 2021; 28: e2766.

Wang

Faming

, et al. A real-time bridge crack detection method based on an improved inception-resnet-v2 structure. IEEE Access 2021; 9: 93209–93223.

Yuan

Wang

Cai

, et al. A multi-scale re-parameterization enhanced bilateral lightweight crack detection model for low-quality environments. Multimedia Tools Appl. Epub ahead of print December 2023. DOI: 10.1007/s11042-023-17664-0.

10.

Zhang

Qian

Tan

Automated bridge crack detection method based on lightweight vision models. Complex Intell Syst 2023; 9: 1639–1652.

11.

Iandola

Han

Moskewicz

, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv preprint, arXiv:160207360, 2016.

12.

Howard

Zhu

Chen

, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint, arXiv:170404861, 2017.

13.

Zhang

Zhou

Lin

, et al. Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, 2018, pp. 6848–6856.

14.

Chen

Yao

, et al. The classification and localization of crack using lightweight convolutional neural network with CBAM. Eng Struct 2023; 275: 115291.

15.

Howard

Sandler

Chu

, et al. Searching for mobilenetv3. In: Proceedings of the IEEE/CVF international conference on computer vision, Seoul, Korea (South), 2019, pp. 1314–1324.

16.

Sepahvand

Abdali-Mohammadi

Taherkordi

An adaptive teacher–student learning algorithm with decomposed knowledge distillation for on-edge intelligence. Eng Appl Artif Intell 2023; 117: 105560.

17.

Chen

Liu

Hou

J-a.

A lightweight deep learning network based on knowledge distillation for applications of efficient crack segmentation on embedded devices. Struct Health Monit 2023; 22: 3027–3046.

18.

Wang

Han

, et al. A lightweight crack segmentation network based on knowledge distillation. J Build Eng 2023; 76: 107200.

19.

Hinton

Vinyals

Dean

Distilling the knowledge in a neural network. arXiv preprint, arXiv:150302531, 2015.

20.

Xie

Girshick

Dollár

, et al. Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, 2017, pp. 1492–1500.

21.

Ren

Qiao

, et al. Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing. Struct Control Health Monit 2020; 27: e2620.

22.

Wang

Zhao

, et al. Progressive blockwise knowledge distillation for neural network acceleration. In: IJCAI, Stockholm, Sweden, 2018, pp. 2769–2775.

23.

Koratana

Kang

Bailis

, et al. Lit: learned intermediate representation training for model compression. In: International conference on machine learning, Long Beach, California, 2019, pp. 3509–3518. PMLR.

24.

Wang

Huo

, et al. Progressive cross-camera soft-label learning for semi-supervised person re-identification. IEEE Trans Circuits Syst Video Technol 2020; 30: 2815–2829.

25.

Yuan

Tay

, et al. Revisiting knowledge distillation via label smoothing regularization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, 2020, pp. 3903–3911.

26.

Cui

Guo

, et al. Beyond self-supervision: a simple yet effective network distillation alternative to improve backbones. arXiv preprint, arXiv:210305959, 2021.