A Faster RCNN Airport Pavement Crack Detection Method Based on Attention Mechanism

Authors

  • Shougang Hao
  • Liming Shao
  • Sibo Wang

DOI:

https://doi.org/10.54097/ajst.v4i2.4122

Keywords:

Faster RCNN, MobileNetV2, CBAM.

Abstract

 Airport pavement inspection is an important link to ensure the safe takeoff and landing of aircraft. At present, the airport pavement safety inspection is still dominated by manual inspection. This method has some problems such as low detection efficiency, strong subjectivity, and unable to fully cover. In this context, Faster RCNN network was used for tunnel crack detection, the backbone network of Faster RCNN was modified, the lightweight network MobileNetV2 was used to improve the network training speed, and CBAM attention mechanism was added to improve the feature extraction ability of the model.

Downloads

Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">

References

Zou Z, Shi Z, Guo Y, et al. 2019. Object Detection in 20 Years: A Survey[J]. arXiv:1905.05055

Girshick R, Donahue J, Darrell T, et al. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Columbus, OH, USA, 580-587.

Girshick R. 2015. Fast R-CNN[C]// 2015 IEEE International Conference on Computer Vision(ICCV), Santiago, Chile, 1440-1448.

Ren S, He K, Girshick R, et al. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks[C] // Advances in neural information processing systems 2015, 91-99.

Woo S, Park J, Lee J Y, et al. 2018. CBAM: Convolutional Block Attention Module[J]. 2018 European Conference on Computer Vision, 3-19.

He K, Zhang X, Ren S, et al. 2016. Deep residual learning for image recognition[C] // 2016 IEEE Conference on Computer Vision and Pattern Recognition, 770-778.

He K, Zhang X, Ren S, et al. 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9): 1904-1916.

Tong Z, Gao J, Han Z Q, et al. 2017a. Recognition of asphalt pavement crack length using deep convolutional neural networks[J]. Road Material and Pavement Design, 19(6): 1334-1349.

Downloads

Published

04-01-2023

How to Cite

Hao, S., Shao, L., & Wang, S. (2023). A Faster RCNN Airport Pavement Crack Detection Method Based on Attention Mechanism. Academic Journal of Science and Technology, 4(2), 129–132. https://doi.org/10.54097/ajst.v4i2.4122

Issue

Section

Articles