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http://dx.doi.org/10.12815/kits.2019.18.3.95

Performance Enhancement Algorithm using Supervised Learning based on Background Object Detection for Road Surface Damage Detection  

Shim, Seungbo (Korea Institute of Civil Engineering and Building Technology)
Chun, Chanjun (Korea Institute of Civil Engineering and Building Technology)
Ryu, Seung-Ki (Korea Institute of Civil Engineering and Building Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.18, no.3, 2019 , pp. 95-105 More about this Journal
Abstract
In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.
Keywords
Road surface damage; Deep neural network; Road maintenance; Region based convolutional neural networks; Background object detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Nair V. and Hinton G. E.(2010), "Rectified linear units improve restricted boltzmann machines," in Proc. the 27th International Conference on Machine Learning (ICML), Haifa, Israel, pp.807-814.
2 Uijlings J. R., Van De Sande K. E., Gevers T. and Smeulders A. W.(2013), "Selective search for object recognition," International Journal of Computer Vision, vol. 104, no. 2, pp.154-171.   DOI
3 Yang X., Li H., Yu Y., Luo X., Huang X. and Yang X.(2018), "Automatic pixel-level crack detection and measurement using fully convolutional networks," Computer-Aided Civil and Infrastructure Engineering, vol. 33, no. 12, pp.1090-1109.   DOI
4 Yu F. and Koltun V.(2015), "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv:1511.07122.
5 Zhang A., Wang K. C., Li B., Yang E., Dai X., Peng Y., Fei Y., Liu Y., Li J. Q. and Chen C.(2017), "Automated pixel-level pavement crack detection on 3Dasphalt surfaces using a deep-learning network," Computer-Aided Civil and Infrastructure Engineering, vol. 32, no. 10, pp.805-819.   DOI
6 Goodfellow I., Bengio Y. and Courville A.(2016), Deep Learning, MIT Press, Cambridge, MA.
7 Chun C., Shim S., Kang S. and Ryu S.(2018), "Development and evaluation of automatic pothole detection using fully convolutional neural networks," Journal of Korea Institute of Intelligent Transport System, vol. 17, no. 5, pp.55-64.
8 Girshick R.(2015), "Fast r-cnn," in Proc. The IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp.1440-1448.
9 Glorot X. and Bengio Y.(2010), "Understanding the difficulty of training deep feedforward neural networks," in Proc. the thirteenth international conference on artificial intelligence and statistics, Sardinia, Italy, pp.249-256.
10 Ioffe S. and Szegedy C.(2015), "Batch normalization: accelerating deep network training by reducing internal covariate shift," in Proc. the 32nd International Conference on Machine Learning (ICML), Lille, France, pp.448-456.
11 Jo Y. and Ryu S.(2015), "Pothole detection system using black-box camera," Sensors, vol. 15, no. 11, pp.29316-29331.   DOI
12 Kingma D. P. and Ba J. L.(2015), "ADAM: a method for stochastic optimization," in Proc. third International Conference on Learning Representations (ICLR), San Diego, CA, pp.1-15.
13 Maeda H., Sekimoto Y., Seto T., Kashiyama T. and Omata H.(2018), "Road damage detection and classification using deep learning neural networks with smartphone images," Computer-Aided Civil and Infrastructure Engineering, vol. 33, no. 12, pp.1127-1141.   DOI