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http://dx.doi.org/10.12815/kits.2020.19.2.89

Encoder Type Semantic Segmentation Algorithm Using Multi-scale Learning Type for Road Surface Damage Recognition  

Shim, Seungbo (Korea Institute of Civil Engineering and Building Technology, Future Infrastructure Research Center)
Song, Young Eun (Hoseo University, Department of Electrical Engineering)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.2, 2020 , pp. 89-103 More about this Journal
Abstract
As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.
Keywords
Road surface damage detection; Encoder type deep learning; Driving safety; Semantic segmentation; Personal mobility;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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