Abstract
Target detection technology has been widely used, while it is less applied in portable equipment as it has certain requirements for devices. For instance, the inventory of rebar is still manually counted at present. In this paper, a lightweight network that adapts mobile devices is proposed to accomplish the task more intelligently and efficiently. Based on the existing method of detection and recognition of dense small objects, the research of rebar recognition was implemented. After designing the multi-resolution input model and training the data set of rebar, the efficiency of detection was improved significantly. Experiments prove that the method proposed has the advantages of higher detection degree, fewer model parameters, and shorter training time for rebar recognition.
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
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Lin, T.-Y., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Liu, S., Huang, D.: Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Redmon, J., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Tianshu, W., Zhijia, Z., Yunpeng, L., Wenhui, P., Hongye, C.: A lightweight small object detection algorithm based on improved SSD. Infrared Laser Eng. 47(7), 703005 (2018)
Wen-xu, S.H.I., Dai-lun, T.A.N., Sheng-li, B.A.O.: Feature enhancement SSD algorithm and its application in remote sensing images target detection. Acta Photonica Sinica 49(1), 128002 (2020)
Moran, J., Lu, H., Wang, Z., He, M., Chang, Z., Hui, B.: Improved YOLO V3 algorithm and its application in small target detection. Acta Opt. Sinica 39(7), 0715004 (2019)
Chen, Z.B., Ye, D.Y., Zhu, C.X., Liao, J.K.: Object recognition method based on improved YOLOv3. Comput. Syst. Appl. 29(1), 49–58 (2020)
Jiangrong, X., et al.: Enhancement of single shot multibox detector for aerial infrared target detection. Acta Opt. Sinica 39(6), 0615001 (2019)
Xiaoning, L.I., et al.: Detecting method of small vehicle targets based on improved SSD. J. Appl. Opt. 41(1), 150–155 (2020)
Shan, Y., Yang, J., Wu, S., et al.: Skip feature pyramid network with a global receptive field for small object detection. CAAI Trans. Intell. Syst. 14(06), 1144–1151 (2019)
Lei, J., Gao, C., Hu, J., Gao, C., Sang, N.: Orientation adaptive YOLOv3 for object detection in remote sensing images. In: Lin, Z., et al. (eds.) PRCV 2019. LNCS, vol. 11857, pp. 586–597. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31654-9_50
Acknowledgment
This work is partially supported by Hainan Science and Technology Project, which is Research and development of intelligent customer service system based on deep learning (No. ZDYF2018017). Thanks to Professor Caimao Li, the correspondent of this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Qu, F., Li, C., Peng, K., Qu, C., Lin, C. (2020). Research on Detection and Identification of Dense Rebar Based on Lightweight Network. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_32
Download citation
DOI: https://doi.org/10.1007/978-981-15-7981-3_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7980-6
Online ISBN: 978-981-15-7981-3
eBook Packages: Computer ScienceComputer Science (R0)