1. Railway Engineering Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China 2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
A bolt detection model algorithm based on self attention mechanism and center point regression (SACPR) was proposed based on the real bridge bolt scene data set. Firstly, a high-quality bridge bolt scene data set based on the real scene was constructed, and for the problems of data imbalance and insufficient diversity, data enhancement method was used to expand the data, so as to obtain higher classification accuracy. Secondly, SACPR model algorithm based on deep learning framework was used to detect bolts in different scenes, and label them. Finally, the validity of the proposed method was verified by the verification experiment of bolt detection accuracy. Comparison was conducted with the results of YOLOv3, Faster-RCNN and RetinaNet, and results showed that the recognition accuracy of the three detection methods was 80.56%, 87.71% and 93.89% respectively, while the recognition accuracy of SACPR model method was 93.91%. The accuracy of SACPR model method was obviously better than that of YOLOv3 model algorithm and Faster-RCNN model algorithm. Although the recognition accuracy was almost the same as that of RetinaNet model algorithm, the detection speed of SACPR model method was 5.6 times of that of RetinaNet model.
Fig.1Example of image sample of bolt detection data set
Fig.2Schematic diagram of SACPR algorithm framework
Fig.3Data preprocessing of bolt detection dataset
IR/像素
AS
EF
NOFC
NOROS
CSS
$ 608\times 608\times 3 $
卷积
?
32
1
2
$ 304\times 304\times 32 $
bottleneck
1
16
1
1
$ 304\times 304\times 16 $
bottleneck
6
24
2
2
$ 152\times 152\times 24 $
bottleneck
6
32
3
1
Tab.1Backbone network architecture parameters in SACPR algorithm
Fig.4Schematic diagram of bolt position prediction
算法
mAP/ %
v/(帧·s?1)
YOLOv3[19] （Backbone: darknet19）
80.56
25.00
YOLOv3[19] (Backbone: MobileNetV2)
78.81
36.00
Faster-RCNN[18]
87.71
5.80
RetinaNet[17]
93.89
7.94
SACPR
93.91
45.00
Tab.2Comparative experiments results of different algorithms
Fig.5Comparison of visualization results of different detection models for bolt images
Fig.6Comparison of visualization results of different detection models of hard sampes for bolt images
[1]
张清华, 卜一之, 李乔 正交异性钢桥面板疲劳问题的研究进展[J]. 中国公路学报, 2017, 30 (3): 14- 30 ZHANG Qing-hua, BU Yi-zhi, LI Qiao Review on fatigue problems of orthotropic steel bridge deck[J]. China Journal of Highway and Transport, 2017, 30 (3): 14- 30
doi: 10.3969/j.issn.1001-7372.2017.03.002
[2]
鞠晓臣 U肋-面板全熔透焊接接头疲劳性能试验研究[J]. 中国公路学报, 2019, 32 (11): 176- 182 JU Xiao-chen Experimental study on fatigue performance of full penetration welded joints of Ushaped rib-plate[J]. China Journal of Highway and Transport, 2019, 32 (11): 176- 182
[3]
赵欣欣, 刘晓光, 潘永杰 正交异性钢桥面板纵肋腹板与面板连接构造的疲劳试验研究[J]. 中国铁道科学, 2013, 32 (2): 41- 45 ZHAO Xin-xin, LIU Xiao-guang, PAN Yong-jie Fatigue test study on the joint structure between the deck and longitudinal rib web of orthotropic steel bridge deck[J]. China Railway Science, 2013, 32 (2): 41- 45
[4]
钟新谷, 彭雄, 沈明燕 基于无人飞机成像的桥梁裂缝宽度识别可行性研究[J]. 土木工程学报, 2019, 52 (4): 52- 61 ZHONG Xin-gu, PENG Xiong, SHEN Ming-yan Feasibility study on bridge crack width identification based on UAV imaging[J]. China Civil Engineering Journal, 2019, 52 (4): 52- 61
[5]
CHOU J C, O’NEILLWENDE A, CHENGH D Pavement distress evaluation using fuzzy logic and moment in variants[J]. Transportation Research Record, 1995, 1505 (1): 39- 46
[6]
NGUYEN N H, KAM T Y, CHENG P Y An automatic approach for accurate edge detection of concrete crack utilizing 2D geometric features of crack[J]. Journal of Signal Processing System, 2014, 77 (3): 221- 240
doi: 10.1007/s11265-013-0813-8
[7]
CHA Y J, CHOI W, BÜYÜKÖZTÜRK O Deep learning-based crack damage detection using convolutional neural networks[J]. Compute-aided Civil and Infrastructure Engineering, 2017, 32 (5): 361- 378
doi: 10.1111/mice.12263
[8]
沙爱民, 童峥, 高杰 基于卷积神经网络的路表病害识别与测量[J]. 中国公路学报, 2018, 31 (1): 1- 10 SHA Ai-min, TONG Zheng, GAO Jie Road surface disease recognition and measurement based on convolutional neural network[J]. China Journal of Highway and Transport, 2018, 31 (1): 1- 10
doi: 10.3969/j.issn.1001-7372.2018.01.001
[9]
韩晓健, 赵志成, 沈泽江 卷积神经网络在桥梁结构表面病害检测中的应用研究[J]. 结构工程师, 2019, 35 (2): 106- 111 HAN Xiao-jian, ZHAO Zhi-cheng, SHEN Ze-jiang Research on the application of convolutional neural network in the detection of bridge structure surface diseases[J]. Structural Engineers, 2019, 35 (2): 106- 111
doi: 10.3969/j.issn.1005-0159.2019.02.014
[10]
赵欣欣, 钱胜胜, 刘晓光 基于卷积神经网络的铁路桥梁高强螺栓缺失图像识别方法[J]. 中国铁道科学, 2018, 39 (4): 56- 62 ZHAO Xin-xin, QIAN Sheng-sheng, LIU Xiao-guang Recognition method for missing images of railway bridge high-strength bolts based on convolutional neural network[J]. China Railway Science, 2018, 39 (4): 56- 62
doi: 10.3969/j.issn.1001-4632.2018.04.09
[11]
李良福, 马卫飞, 李丽 基于深度学习的桥梁裂缝检测算法研究[J]. 自动化学报, 2019, 6: 1- 16 LI Liang-fu, MA Wei-fei, LI Li Research on bridge crack detection algorithm based on deep learning[J]. Acta Automatica Sinica, 2019, 6: 1- 16
[12]
CHA Y J, CHOI W, SUH G Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types[J]. Computer-aided Civil and Infrastructure Engineering, 2018, 33 (4): 1- 17
[13]
ZHANG C, CHANG C, JAMSHIDI M Concrete bridge surface damage detection using a single-stage detector[J]. Computer-aided Civil and Infrastructure Engineering, 2020, 35 (4): 389- 409
[14]
LUVIZON D C, PICARD D, TABIA H HediTabia: multi-task deep learning for real-time 3D human pose estimation and action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43 (8): 2752- 2764
[15]
ZHOU X, WANG D, KRÄHENBÜHL P. Objects as points [EB/OL]. [2019-04-16]. https://arxiv.org/abs/1904.07850.
[16]
SANDLER M, HOWARD A, ZHU M. Mobilenetv2: invert residuals and linear bottlenecks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018, 4510-4520.
[17]
LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection [C]// Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980-2988.
[18]
REN S, HE K, 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, 2016, 39 (6): 1137- 1149
