ABSTRACT
Automated visual detection and quantification of road defects has been a hot research topic for quite a long time due to its practical importance for road maintenance and traffic safety. However, uncertainties associated with the 2D images, such as non-uniformity of defects, insufficient background illumination, and etc., make it a challenging problem. This research work aims to solve the problem by employing a deep learning based approach. Specifically, image segmentation has been carried out, using a convolutional encoder-decoder model, to segment various defects from the non-defect area of the road. The method lead to a reasonable segmentation of different defects. Consequently, the extracted defect areas, in terms of number of pixels, are used to derive road condition indices being followed in Germany. In comparison, the indices derived using deep learning based approach are found to more accurate than those derived using conventional approach.
- 2006. Zusätzliche Technische Vertragsbedingungen und Richtlinien zur Zustandserfassung und -bewertung von Straßen.2006, geänderter und korrigierter Nachdruck 2018 unter Berücksichtigung des BMV ARS 6/2018 (2006).Google Scholar
- Francois Blais, Marc Rioux, and J.-Angelo Beraldin. 1988. Practical Considerations For A Design Of A High Precision 3-D Laser Scanner System. In Optomechanical and Electro-Optical Design of Industrial Systems, Robert J. Bieringerand Kevin G. Harding (Eds.). Vol. 0959. International Society for Optics and Photonics, SPIE, 225 – 246. https://doi.org/10.1117/12.947787Google Scholar
- Wenming Cao, Qifan Liu, and Zhiquan He. 2020. Review of Pavement Defect Detection Methods. IEEE Access 8(2020), 14531–14544. https://doi.org/10.1109/ACCESS.2020.2966881Google ScholarCross Ref
- G Caroff, P Joubert, F Prudhomme, and G Soussain. 1989. Classification of pavement distresses by image processing (MACADAM SYSTEM). In Proc. ASCE. 46–51.Google Scholar
- Sylvie Chambon and Jean-Marc Moliard. 2011. Automatic Road Pavement Assessment with Image Processing: Review and Comparison. International Journal of Geophysics 2011 (2011), 1–20.Google ScholarCross Ref
- Tom B.J. Coenen and Amir Golroo. 2017. A review on automated pavement distress detection methods. Cogent Engineering 4, 1 (2017), 1374822. https://doi.org/10.1080/23311916.2017.1374822Google ScholarCross Ref
- Mark David Jenkins, Thomas Arthur Carr, Maria Insa Iglesias, Tom Buggy, and Gordon Morison. 2018. A Deep Convolutional Neural Network for Semantic Pixel-Wise Segmentation of Road and Pavement Surface Cracks. In 2018 26th European Signal Processing Conference (EUSIPCO). 2120–2124. https://doi.org/10.23919/EUSIPCO.2018.8553280Google ScholarCross Ref
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, K. Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), 248–255.Google ScholarCross Ref
- Wang Dong and Zhou Shisheng. 2008. Color Image Recognition Method Based on the Prewitt Operator. In 2008 International Conference on Computer Science and Software Engineering, Vol. 6. 170–173. https://doi.org/10.1109/CSSE.2008.567Google ScholarDigital Library
- Li Er-sen, Zhu Shu-long, Zhu Bao-shan, Zhao Yong, Xia Chao-gui, and Song Li-hua. 2009. An Adaptive Edge-Detection Method Based on the Canny Operator. In 2009 International Conference on Environmental Science and Information Application Technology, Vol. 1. 465–469. https://doi.org/10.1109/ESIAT.2009.49Google ScholarDigital Library
- David A. Forsyth and Jean Ponce. 2012. Computer Vision - A Modern Approach, Second Edition.Pages 14-29, Pitman. 1–791 pages.Google Scholar
- Kasthurirangan Gopalakrishnan. 2018. Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review. Data 3, 3 (2018). https://doi.org/10.3390/data3030028Google Scholar
- Hyun-Woo Cho Ju-Yeong Jung, Hyuk-Jin Yoon. 2018. A study on crack depth measurement in steel structures using image-based intensity differences. Adv. Civil Eng. 2018(2018), 1–10. https://doi.org/10.1155/2018/7530943Google ScholarCross Ref
- Shilpa Kamdi and Radha Krishna. 2012. Image Segmentation and Region Growing Algorithm.Google Scholar
- N. Kanopoulos, N. Vasanthavada, and R.L. Baker. 1988. Design of an image edge detection filter using the Sobel operator. IEEE Journal of Solid-State Circuits 23, 2 (1988), 358–367. https://doi.org/10.1109/4.996Google ScholarCross Ref
- Byoung Jik Lee and Hosin “David” Lee. 2004. Position-Invariant Neural Network for Digital Pavement Crack Analysis. Computer-Aided Civil and Infrastructure Engineering 19, 2(2004), 105–118. https://doi.org/10.1111/j.1467-8667.2004.00341.x arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-8667.2004.00341.xGoogle Scholar
- Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common Objects in Context. In ECCV. 740–755.Google Scholar
- Senthan Mathavan, Khurram Kamal, and Mujib Rahman. 2015. A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements. Trans. Intell. Transport. Sys. 16, 5 (Oct. 2015), 2353–2362. https://doi.org/10.1109/TITS.2015.2428655Google ScholarDigital Library
- Microsoft. 2019. Visual Object Tagging Tool. https://github.com/microsoft/VoTTGoogle Scholar
- Yong Shi, Limeng Cui, Zhiquan Qi, Fan Meng, and Zhensong Chen. 2016. Automatic Road Crack Detection Using Random Structured Forests. IEEE Transactions on Intelligent Transportation Systems 17, 12(2016), 3434–3445. https://doi.org/10.1109/TITS.2016.2552248Google ScholarDigital Library
- L Sjogren and Petra Offrell. 2000. Automatic crack measurement in Sweden. In SURF 2000: Fourth International Symposium on pavement Surface Characteristics on Roads and AirfieldsWorld Road Association (PIARC).Google Scholar
- Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. 2019. Deep High-Resolution Representation Learning for Human Pose Estimation. In CVPR.Google Scholar
- KC Wang, Zhiqiong Hou, and Weiguo Gong. 2008. Automation techniques for digital highway data vehicle (DHDV). In Proc. 7th Int. Conf. Manag. Pavement Assets. Citeseer.Google Scholar
- Wikipedia contributors. 2021. Image segmentation — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Image_segmentation&oldid=1053962860 [Online; accessed 14-November-2021].Google Scholar
- Fan Yang, Lei Zhang, Sijia Yu, Danil Prokhorov, Xue Mei, and Haibin Ling. 2019. Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection. arxiv:1901.06340 [cs.CV]Google Scholar
- Lei Zhang, Fan Yang, Yimin Daniel Zhang, and Ying Julie Zhu. 2016. Road crack detection using deep convolutional neural network. In 2016 IEEE International Conference on Image Processing (ICIP). 3708–3712. https://doi.org/10.1109/ICIP.2016.7533052Google ScholarCross Ref
- Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. 2018. Semantic Understanding of Scenes through the ADE20K Dataset. International Journal on Computer Vision (IJCV) (2018).Google Scholar
Index Terms
- A Semantic Segmentation Approach for Road Defect Detection and Quantification
Recommendations
On-Road Vehicle Detection: A Review
Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a ...
Nighttime vehicle light detection on a moving vehicle using image segmentation and analysis techniques
This study proposes a vehicle detection system for identifying the vehicles by locating their headlights and rear-lights in the nighttime road environment. The proposed system comprises of two stages for detecting the vehicles in front of the camera-...
Semantic segmentation-based parking space detection with standalone around view monitoring system
An auto-parking system is one of the promising technologies to reduce accidents and enhance driver convenience in parking lots. To accomplish collision-free parking, precise and robust parking space detection is required. However, harsh conditions such ...
Comments