Rapid inspection on installation quality of temporary support frame in Winter Olympic venues

doi:10.13474/j.cnki.11-2246.2023.0080

Abstract

Abstract: Most of the infrastructure for the 2022 Beijing Winter Olympics are temporary facilities,which are mainly built on scaffolding. Bolts are required for pinning and linking of "skeletons". However, there are hundreds of thousands of bolts in the entire venue, which means the manual detection is not realistic.Therefore, it is urgent to build a system that can automatically and real-timely detect the installation quality of the bolts on the support frame. A detection method of bolt installation quality based on Faster RCNN target detection network is proposed in this paper. In addition, a support frame safety detection software and four-camera combined hardware equipment are developed independently, which greatly improves work efficiency. The strategy not only realizes automatic safety inspection, but also ensures more than 97% detection accuracy, ensuring the safety of temporary facilities for the Winter Olympics.

Key words: Faster RCNN target detection network, security detection, automatic real-time detection, 2022 Beijing Winter Olympics

CLC Number:

P258

HE Yifeng, ZOU Jingui, GONG Xiaoqiang, ZHANG Lan. Rapid inspection on installation quality of temporary support frame in Winter Olympic venues[J]. Bulletin of Surveying and Mapping, 2023, 0(3): 99-103.

References

[1] 雍黎, 单倩澜. 冬奥临时设施安全这些"黑科技"保驾护航[N]. 科技日报, 2022-01-07(2).
[2] 龚小强, 邹进贵, 孟丽媛. 基于目标检测和角点检测的近景摄影测量控制点自动提取[J]. 测绘通报, 2020(S1):173-175.
[3] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110.
[4] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). San Diego, CA:IEEE, 2005:886-893.
[5] GIRSHICK R, DONAHUE J, DARRELL T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus, OH:IEEE, 2014:580-587.
[6] UIJLINGS J R, VAN DE SANDE K E A, GEVERS T, et al.Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2):154-171.
[7] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago:IEEE, 2016:1440-1448.
[8] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[9] 马京振, 陈换新, 朱新铭, 等. 利用Faster R-CNN进行立交桥自动识别与定位[J]. 测绘通报, 2021(3):28-32.
[10] 程瑞, 高建, 邢强, 等. 一种优化的Faster R-CNN小目标检测方法[J]. 测绘通报, 2021(9):21-27.
[11] 冯小雨, 梅卫, 胡大帅. 基于改进Faster R-CNN的空中目标检测[J]. 光学学报, 2018, 38(6):250-258.
[12] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2022-07-01]. https://arxiv.org/abs/1409.1556.
[13] 王忠塬. 基于改进Faster RCNN的小目标检测技术研究[D]. 长春:长春理工大学, 2021.

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