Abstract
Deep learning (DL) has proven its efficacy in extracting useful distress information from image-based data of infrastructure assets, such as pavements. Despite the overwhelming research on this topic, state-of-the-art DL approaches fail to perform satisfactorily on independent datasets as noted from an object detection-based competition. Besides, a lack of clarification in computing DL performance measures and inadequate discussion on DL implementation framework still exist. To this end, this paper contributes to the body of knowledge by synthesizing the performance of DL models from the existing relevant literature using the ‘random effect meta-analysis’ approach. Meta-analysis requires an estimate of the uncertainty in the reported performance measure (i.e., F1-score) to assign weights to individual studies and compute an overall performance measure for a group of studies. Hence, this paper introduces a statistical approach to calculate the uncertainty in the reported F1-score to compute the within-study variance. The methods, statistics, and results presented in this paper will help understand the requisites for future studies on DL in pavement distress evaluation, ultimately improving pavement asset management.
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References
Herold M, Roberts D, Noronha V, Smadi O (2008) Imaging spectrometry and asphalt road surveys. Transp Res Part C Emerg Technol 16:153–166. https://doi.org/10.1016/j.trc.2007.07.001
Bang S, Park S, Kim H, Kim H (2019) Encoder–decoder network for pixel-level road crack detection in black-box images. Comput Aided Civ Infrastruct Eng 34:713–727
Zhang A, Wang KC, Fei Y et al (2019) Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Comput Aided Civ Infrastruct Eng 34:213–229
Huyan J, Li W, Tighe S et al (2020) CrackU-net: a novel deep convolutional neural network for pixelwise pavement crack detection. Struct Control Health Monit 27:2551
Hascoet T, Zhang Y, Persch A, et al (2020) FasterRCNN monitoring of road damages: competition and deployment. In: 2020 IEEE International conference on big data (Big Data. IEEE, pp 5545–5552
Naddaf-Sh S, Naddaf-Sh MM, Kashani AR, Zargarzadeh H (2020) An efficient and scalable deep learning approach for road damage detection. In: 2020 IEEE International conference on big data big data. IEEE, pp 5602–5608
Mandal V, Mussah AR, Adu-Gyamfi Y (2020) Deep learning frameworks for pavement distress classification: A comparative analysis. In: 2020 IEEE international conference on big data Big Data. IEEE, pp 5577–5583
Arya D, Maeda H, Ghosh SK, et al (2020) Global road damage detection: State-of-the-art solutions. In: 2020 IEEE international conference on big data (Big Data. IEEE, pp 5533–5539
Hsieh YA, Tsai YJ (2020) Machine learning for crack detection: review and model performance comparison. J Comput Civ Eng 34:04020038
Hoang ND (2018) Detection of surface crack in building structures using image processing technique with an improved Otsu method for image thresholding. Adv Civ Eng. https://doi.org/10.1155/2018/3924120
Wang W, Su C (2020) Convolutional neural network-based pavement crack segmentation using pyramid attention network. IEEE Access 8:206548–206558
Mathavan S, Vaheesan K, Kumar A et al (2017) Detection of pavement cracks using tiled fuzzy Hough transform. J Electron Imag 26:053008
Ghosh R, Smadi O (2021) Automated detection and classification of pavement distresses using 3D pavement surface images and deep learning. Transp Res Record. 03611981211007481
Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629
Tang S, Chen Z (2020) Scale-space data augmentation for deep transfer learning of crack damage from small sized datasets. J Nondestr Eval 39:1–18
Abdellatif M, Peel H, Cohn AG, Fuentes R (2021) Combining block-based and pixel-based approaches to improve crack detection and localisation. Autom Constr 122:103492
Bolya D, Foley S, Hays J, Hoffman J (2020) Tide: a general toolbox for identifying object detection errors. In: Computer Vision–ECCV 2020: 16th European Conference. Springer, Glasgow, UK, pp 16 558–573
Zhang Y, Chen B, Wang J et al (2020) APLCNet: automatic pixel-level crack detection network based on instance segmentation. IEEE Access 8:199159–199170
Tiu E (2019) Metrics to evaluate your semantic segmentation model|by Ekin Tiu|Towards Data Science. https://towardsdatascience.com/metrics-to-evaluate-your-semantic-segmentation-model-6bcb99639aa2. Accessed 20 Aug 2023
Zhang K, Zhang Y, Cheng HD (2020) Self-supervised structure learning for crack detection based on cycle-consistent generative adversarial networks. J Comput Civ Eng 34:04020004
Mei Q, Gül M (2020) A cost effective solution for pavement crack inspection using cameras and deep neural networks. Constr Build Mater 256:119397. https://doi.org/10.1016/j.conbuildmat.2020.119397
Lang H, Lu JJ, Lou Y, Chen S (2020) Pavement cracking detection and classification based on 3D image using multiscale clustering model. J Comput Civ Eng 34:04020034
Pan Y, Chen X, Sun Q, Zhang X (2021) Monitoring asphalt pavement aging and damage conditions from low-altitude UAV imagery based on a CNN approach. Can J Remote Sens 47:432–449
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91–99
Tran TS, Tran VP, Lee HJ et al (2020) A two-step sequential automated crack detection and severity classification process for asphalt pavements. Int J Pav Eng 23:2019
Lõuk R, Riid A, Pihlak R, Tepljakov A (2020) Pavement defect segmentation in orthoframes with a pipeline of three convolutional neural networks. Algorithms 13:198
Alfarraj O (2020) Internet of things with bio-inspired co-evolutionary deep-convolution neural-network approach for detecting road cracks in smart transportation. Neural Comput Appl.
Feng X, Xiao L, Li W et al (2020) Pavement crack detection and segmentation method based on improved deep learning fusion model. Math Prob Eng. https://doi.org/10.1155/2020/8515213
Maeda H, Sekimoto Y, Seto T et al (2018) Road damage detection and classification using deep neural networks with smartphone images. Comput Aided Civ Infrastruct Eng 33:1127–1141
Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2021) Introduction to meta-analysis, 2nd edn. Wiley
Varghese V, Chikaraishi M, Urata J (2020) Deep learning in transport studies: a meta-analysis on the prediction accuracy. J Big Data Anal Transp. 2:199
Bashar MZ, Torres-Machi C (2021) Performance of machine learning algorithms in predicting the pavement international roughness index. Transp Res Record 2675:226
Papadimitriou E, Theofilatos A (2017) Meta-analysis of crash-risk factors in freeway entrance and exit areas. J Transp Eng Part A Syst 143:04017050
Alruqi WM, Hallowell MR (2019) Critical success factors for construction safety: review and meta-analysis of safety leading indicators. J Constr Eng Manag 145:04019005
Elvik R (2005) Introductory guide to systematic reviews and meta-analysis. Transp Res Rec 1908:230–235
Deeks JJ, Altman DG (2001) Effect measures for meta-analysis of trials with binary outcomes. Syst Rev Health Care Meta-Anal Context. https://doi.org/10.1002/9780470693926.ch16
Zhang X, Rajan D, Story B (2019) Concrete crack detection using context-aware deep semantic segmentation network. Comput Aided Civ Infrastruct Eng 34:951–971
Liu J, Yang X, Lau S et al (2020) Automated pavement crack detection and segmentation based on two-step convolutional neural network. Comput Aided Civ Infrastruct Eng 35:1291–1305
Majidifard H, Jin P, Adu-Gyamfi Y, Buttlar WG (2020) Pavement image datasets: a new benchmark dataset to classify and densify pavement distresses. Transp Res Rec 2674:328–339
Peraka NSP, Biligiri KP, Kalidindi SN (2021) Development of a multi-distress detection system for asphalt pavements: transfer learning-based approach. Transp Res Record 75:538
Roberts R, Giancontieri G, Inzerillo L, Mino G (2020) Towards low-cost pavement condition health monitoring and analysis using deep learning. Appl Sci 10:319
Ibragimov E, Lee HJ, Lee JJ, Kim N (2020) Automated pavement distress detection using region based convolutional neural networks. Int J Pav Eng 23:1981
Lei X, Liu C, Li L, Wang G (2020) Automated pavement distress detection and deterioration analysis using street view map. IEEE Access 8:76163–76172
Chen SY, Zhang Y, Zhang YH et al (2019) Embedded system for road damage detection by deep convolutional neural network. Math Biosci Eng MBE 16:7982–7994
Zhang K, Cheng HD, Zhang B (2018) Unified approach to pavement crack and sealed crack detection using pre-classification based on transfer learning. J Comput Civ Eng 32:04018001
Yu B, Meng X, Yu Q (2021) Automated pixel-wise pavement crack detection by classification-segmentation networks. J Transp Eng Part B Pav 147:04021005
Qiao W, Liu Q, Wu X et al (2021) Automatic pixel-level pavement crack recognition using a deep feature aggregation segmentation network with a scSE attention mechanism module. Sensors 21:2902
Fei Y, Wang KC, Zhang A et al (2019) Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V. IEEE Trans Intell Transp Syst 21:273–284
Yu Y, Guan H, Li D et al (2022) CCapFPN: a context-augmented capsule feature pyramid network for pavement crack detection. IEEE Trans Intell Transp Syst 23:3324–3335. https://doi.org/10.1109/TITS.2020.3035663
Qu Z, Mei J, Liu L, Zhou DY (2020) Crack detection of concrete pavement with cross-entropy loss function and improved VGG16 network model. IEEE Access 8:54564–54573
Chun C, Ryu SK (2019) Road surface damage detection using fully convolutional neural networks and semi-supervised learning. Sensors 19:5501
Jia G, Song W, Jia D, Zhu H (2019) Sample generation of semi-automatic pavement crack labelling and robustness in detection of pavement diseases. Electron Lett 55:1235–1238
Lajeunesse MJ (2021) Fixed effect, homogeneity tests, and random-effects meta-analysis in Microsoft Excel. figshare. Online resource. https://doi.org/10.6084/m9.figshare.14138087.v1
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Zihan, Z.U.A., Smadi, O., Tilberg, M. et al. Synthesizing the performance of deep learning in vision-based pavement distress detection. Innov. Infrastruct. Solut. 8, 299 (2023). https://doi.org/10.1007/s41062-023-01250-2
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DOI: https://doi.org/10.1007/s41062-023-01250-2