Rutting Abnormality Analysis Method for 3D Asphalt Pavement Surfaces Based on Semantic Segmentation Model

doi:10.12141/j.issn.1000-565X.210778

Abstract

Abstract:

Rutting depth is an important indicator for the evaluation of pavement conditions and road maintenance. The existing rutting depth measurement method fails to consider the influence of potholes, raveling, cracks and bridge joints under complex road conditions, and its effectiveness and applicability are limited, and the authenticity of the measurement results remains to be further verified. In view of this, an abnormality detection and correction method based on semantic segmentation model is proposed. The road cross section elevation data is collected by three-dimensional line laser technology, and the rutting depth is extracted by the envelope algorithm. For cross-section of the maximum rut to more than 10 mm, this paper builds a semantic semantic division framework based on deep learning, proposes an improved DeepLabV3+ network to automatically identify and pixel positioning of the disease type, and designs a correction rule based on Lagrangian interpolation to correct the abnormal rut by combining the maximum rut depth elevation points. The results show that the improved DeepLabV3+ model can more accurately identify and locate pavement distress causing rutting abnormality, and its comprehensive detection accuracy of five pavement characteristics and distress reaches 81.63%. Its performance in Mean Intersection over Union (MIoU) and Intersection over Union (IoU) is better than that of U-Net, PSPNet, and DeepLabV3+ models. The field validation results show that the method in the paper can not only automatically analyze the causes of rutting abnormality, but also exclude the influence of other distress by correcting the abnormal rutting, so as to restore the actual rutting depth level to a greater extent. The research results in this paper can provide scientific data support for pavement preventive maintenance.

Key words: 3D line laser technology, rutting abnormality detection, semantic segmentation model, rutting depth, abnormal value correction

CLC Number:

U416.2

WANG Aidi, LANG Hong, DING Shuo, et al. Rutting Abnormality Analysis Method for 3D Asphalt Pavement Surfaces Based on Semantic Segmentation Model[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(1): 134-144.

Figures/Tables 12

Table 1

Fig.1

Fig.2

Fig.3

Fig.4

Table 2

Table 3

Table 4

Fig.5

Fig.6

Fig.7

Fig.8

References 22

1	交通运输部．2020年交通运输行业发展公报［EB/OL］．（2021-05-19）［2021-11-01］．．
2	黄晓明，张晓冰，邓学钧．沥青路面车辙形成规律环道试验研究［J］．东南大学学报（自然科学版），2000，30（5）：96-101．
	HUANG Xiaoming， ZHANG Xiaobing， DENG Xuejun ．Asphalt pavement rutting prediction of high-grade highway［J］．Journal of Southeast University （Natural Science Edition），2000，30（5）：96-101．
3	WANG H ．Development of laser system to measure pavement rutting［D］．Tampa：South Florida University，2005．
4	戴建华．基于养护决策分析需求的路面技术状况检测［J］．中国公路，2020（15）：35-40．
	DAI Jianhua ．Pavement technology condition detection based on maintenance decision analysis［J］．China Highway，2020（15）：35-40．
5	孟娜．基于激光扫描点云的数据处理技术研究［D］．济南：山东大学，2009．
6	ZHANG L， ZHANG Y J， CHEN B ．Improving the extracting precision of stripe center for structured light measurement［J］．Optik，2020，207：163816．
7	马建，赵祥模，贺拴海，等．路面检测技术综述［J］．交通运输工程学报，2017，17（5）：121-137．
	MA Jian， ZHAO Xiangmo， HE Shuanhai，et al ．Review of pavement detection technology［J］．Journal of Traffic and Transportation Engineering，2017，17（5）：121-137．
8	李莉，孙立军，谭生光，等．用于路面车辙检测的线结构光图像处理流程［J］．同济大学学报（自然科学版），2013，41（5）：710-715．
	LI Li， SUN Lijun， TAN Shengguang，et al ．Line-structured light image processing procedure for pavement rut detection［J］．Journal of Tongji University （Natural Science），2013，41（5）：710-715．
9	张德津，李清泉，何莉，等．一种新的激光车辙深度测量方法研究［J］. 光学学报，2013，33（1）：115-121．
	ZHANG Dejin， LI Qingquan， HE Li，et al ．A new method for laser rut depth measurement［J］．Chinese Optics Letters，2013，33（1）：115-121．
10	FENG L. A Methodology for characterizing pavement rutting condition using emerging 3D line laser imaging technology［D］．Atlanta：Georgia Institute of Technology，2012．
11	KAGE T， MATSUSHIMA K ．Method of rut detection using lasers and in-vehicle stereo camera［C］∥Proceedings of IEEE 2015 International Conference on Intelligent Informatics and Biomedical Sciences．New York：IEEE，2015：48-53．
12	李中轶，罗文婷，李林，等．基于路面3D激光点云数据的车辙自动测量与横向定位［J］．传感技术学报，2019，32 （4）：637-642．
	LI Zhongyi， LUO Wenting， LI Lin，et al ．Automatic rutting measurement and lateral positioning based on 3D laser point cloud data of road surface［J］．Journal of Sensing Technology，2019，32 （04）：637-642．
13	罗楠欣．基于路面高精三维图像的车辙识别与评价［D］．成都：西南交通大学，2015．
14	HUANG Y R， COPENHAVER T， HEMPEL P ．Texas department of transportation 3D transverse profiling system for high-speed rut measurement［J］．Journal of Infrastructure Systems，2013，19（2）：221-230．
15	LANG H， LU J， LOU Y X，et al ．Pavement cracking detection and classification based on 3D image using multiscale clustering model［J］．Journal of Computing in Civil Engineering，2020，34（5）：04020034．
16	公路路面技术状况自动化检测规程：［S］．
17	HUANG Y C C， HEMPEL P ．Texas department of transportation 3D profiling system for high-speed rut measurement［J］．Journal of Infrastructure Systems，2013，19：221-230．
18	SIMPSON A L ．Characterization of transverse profile［J］. Journal of the Transportation Research Board，1999，1655（1）：185-191．
19	公路技术状况评定标准：［S］．
20	CHEN L C， ZHU Y， PAPANDREOU G，et al ．Encoder-Decoder with Atrous separable convolution for semantic image segmentation［C］∥Proceedings of the European Conference on Computer Vision （ECCV）．［S.l.］：［s.n.］，2018：801-818．
21	ZHANG A， WANG K C P， AI C ．3D shadow modeling for detection of descended patterns on 3D pavement surface［J］．Journal of Computing in Civil Engineering，2017，31（4）：04017019/1-13．
22	LIN T Y， GOTAL P， GIRSHICK R ．Focal loss for dense object detection［J］．IEEE Transactions on Pattern Analysis & Machine Intelligence，2017，PP（99）：2999-3007．

数据集	图像数量
数据集	裂缝	坑槽	松散	桥接缝
总计	1 689	391	343	602
训练集	1 258	286	252	451
验证集	205	51	49	69
测试集	226	54	42	82

输入图像类型	不同病害下的IoU					MIoU
输入图像类型	背景	坑槽	松散	桥接缝	裂缝	MIoU
二维图像	94.89	41.46	37.40	87.13	39.17	60.01
三维图像	96.06	79.63	80.48	88.27	60.38	80.96
双通道图像	96.54	79.76	82.56	88.86	60.42	81.63

损失函数	不同病害下的IoU					MIoU
损失函数	背景	坑槽	松散	桥接缝	裂缝	MIoU
交叉熵损失函数	96.71	79.32	81.86	88.21	59.66	81.15
焦点损失函数	96.54	79.76	82.56	88.86	60.42	81.63

网络	不同病害下的IoU					MIoU
网络	背景	坑槽	松散	桥接缝	裂缝	MIoU
U-Net	95.67	75.42	78.61	88.01	65.69	80.68
PSPNet	96.04	80.37	78.13	86.35	47.01	77.58
DeepLabV3+with Resnet101	95.48	81.83	77.64	87.69	51.97	78.92
DeepLabV3+with Xception	96.54	79.76	82.56	88.86	60.42	81.63

[1]	DENG Kailing, WANG Duanyi, FANG Qiuping. Fracture Energy Test Method and Influencing Factors of Asphalt-Aggregate Interface [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 12-20.
[2]	Qi LIU Bin Yu. Pavement Crack Recognition Algorithm Based on Transposed CNN [J]. Journal of South China University of Technology(Natural Science Edition), 2021, 49(12): 124-132.
[3]	FAN Zhenyang, CHEN Bing, WANG Xuancang, et al. Variation Laws and Influencing Factors of Heating Characteristics of Concrete Pavement with Wave Absorbing and Ice Melting [J]. Journal of South China University of Technology (Natural Science Edition), 2021, 49(2): 68-78.
[4]	AI Changfa, HUANG Yangquan, LUO Liufen, et al. Estimation of Comprehensive Impact of External Factors on Interlayer Fatigue Life of Asphalt Pavement Based on ISS & SSDR [J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48(5): 58-66,74.
[5]	SONG Bo ZHANG Jinxi XUE Zhongjun YE Shengbo ZHANG Tao. Asphalt Thin-layer Thickness Detection Method Based on WRELAX Algorithm for Time-Delay Estimation [J]. Journal of South China University of Technology (Natural Science Edition), 2018, 46(11): 132-141.
[6]	. Predicting the time-dependent one-dimensional temperature field of pavement using the Laplace transformation [J]. Journal of South China University of Technology (Natural Science Edition), 2017, 45(11): 0-0.
[7]	XIAO Min-min. Mixture Composition Design and Interface Properties of Asphalt Plug Expansion Joint [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(8): 106-113.
[8]	XIAO Min-min. Mechanical Behavior Characteristics of Asphalt Plug Expansion Joint Pavement's Interface [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(6): 105-112.
[9]	Zhang Ke Zhang Zheng-qi. Deterioration of Mechanical Properties of Asphalt Mixture in Salty and Humid Environment#br# [J]. Journal of South China University of Technology (Natural Science Edition), 2015, 43(8): 106-112.
[10]	Zhang Xiao-ning Ｒong Hong-liu Huang Wen-ke Chen Jian-hua . Accelerated Load Testing of High-Temperature Performance of Long-Span MA Steel Bridge Deck Pavement [J]. Journal of South China University of Technology (Natural Science Edition), 2014, 42(12): 21-26,34.
[11]	Wu Zhi- yong Zhang Xiao- ning You Hong Yuan Miao- miao Wan Cheng . Prediction of Fatigue Life of Asphalt Mixture Based on Strain Control [J]. Journal of South China University of Technology (Natural Science Edition), 2014, 42(2): 139-144.
[12]	Chen Hua-xin Zhang Zheng-qi Hu Chang-shun. Low-temperature Anti-cracking Performance of Fiber-reinforced Asphalt Mixture [J]. Journal of South China University of Technology(Natural Science Edition), 2004, 32(4): 82-86.
[13]	Zhang Li-juan, Wang Yi-min, Chen Ye-kai, et al. Compactibility of Ionic Soil Stabilizer Strengthening Soil [J]. Journal of South China University of Technology(Natural Science Edition), 2004, 32(3): 83-87.
[14]	Wang Duan-yi Li Wei-jie Zhang Xiao-ning. Evaluation and Measurement of Asphalt Pavement Surface Texture Depth with Digital Image Technique [J]. Journal of South China University of Technology(Natural Science Edition), 2004, 32(2): 42-45.
[15]	. Evaluation on Grading Type of Asphalt Mixtrue with Virtual Test Method [J]. Journal of South China University of Technology(Natural Science Edition), 2003, 31(2): 48-51.