Abstract
Road damage detection is very important for road maintenance. Deep learning is one of the popular method in road damage detection. Deep learning road damage detection methods include Fast R-CNN, Faster R-CNN, Mask R-CNN, RetinaNet. These methods always have some problems, shallow and deep features can not be extracted simultaneously, the detection accuracy of big targets or small targets is low, the speed of detection is slow. etc. This paper proposes one method based on M2det which can extract the shallow and deep feature. For its multi-scale and multi-level, it belongs to one-stage. We use M2det for road damage detection, training on a large number of images photographed by a vehicle-mounted smartphone, then comparing it with the other one-stage methods. Finally, experimental results show that our method is better than the-state-of-art methods in road damage detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Maeda, H., Sekimoto, Y., Seto, T.: Road damage detection and classification using deep neural networks with smartphone images. Comput. Aid. Civ. Infrastruct. Eng. 33(12), 1127–1141 (2018)
Yang, Y.: Recomputation of dense layers for the performance improvement of DCNN. IEEE Trans. Softw. Eng. PP(99), 1 (2019)
Seungbo, S., Chanjun, C., Ki, R.S.: Road surface damage detection based on object recognition using Fast R-CNN. J. Korea Inst. Intell. Transp. Syst. 18(2), 104–113 (2019)
Wang, W.: Road damage detection and classification with Faster R-CNN. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE (2018)
Singh, J., Shekhar, S.: Road damage detection and classification in smartphone captured images using mask R-CNN. arXiv preprint arXiv:1811.04535 (2018)
Ale, L., Zhang, N., Li, L.: Road damage detection using RetinaNet. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE (2018)
Lin, T., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936–944, Honolulu (2017)
Zhao, Q., Sheng, T., Wang, Y.: M2Det: a single-shot object detector based on multi-level feature pyramid network. Proc. AAAI Conf. Artif. Intell. 33, 9259–9266 (2019)
Zhang, T., Li, L.: An improved object detection algorithm based on M2Det. In: 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pp. 582–585, Dalian (2020)
Pan, Y., Zhang, G., Zhang, L.: A spatial-channel hierarchical deep learning network for pixel-level automated crack detection. In: Automation in Construction, p. 119 (2020)
Theckedath, D., Sedamkar, R.: Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Comput. Sci. 1(2), 1–7 (2020)
Girshick, R: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 11–18 December 2015, pp. 1440–1448 (2015)
Maeda, H., Sekimoto, Y., Seto, T., et al.: Road damage detection and classification using deep neural networks with smartphone images. Comput. Aid. Civ. Infrastruct. Eng. 33(12), 1127–1141 (2018)
Choi, J., Chun, D., Kim, H.: Gaussian YOLOV3: an accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 502–511 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gan, X., Qu, J., Yin, J., Huang, W., Chen, Q., Gan, W. (2021). Road Damage Detection and Classification Based on M2det. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-78615-1_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78614-4
Online ISBN: 978-3-030-78615-1
eBook Packages: Computer ScienceComputer Science (R0)