Lightweight Semantic Segmentation for Road-Surface Damage Recognition Based on Multiscale Learning

• Published in: IEEE access Vol. 8; pp. 102680 - 102690
• Main Authors: Shim, Seungbo, Cho, Gye-Chun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accidents; Algorithms; Artificial neural networks; Coders; Damage; Decoding; Driver behavior; driving safety; Image segmentation; Lightweight; lightweight deep learning; Machine learning; Neural networks; People with disabilities; personal mobility; Road surface; Road-surface damage detection; Roads; Safety; Semantic segmentation; Semantics; Steering; Wheelchairs
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2998427

With an aging society, the demand for personal mobility for disabled and aging people is increasing. As of 2017, the number of electric wheelchairs in Korea was 90,000 according to the domestic government statistics and has since increased continuously. However, people with disabilities and seniors are more likely to be involved in accidents while driving because their judgment and coordination are inferior to those of ordinary people. One of the factors that could lead to accidents is the interference in the vehicle-steering control owing to unbalanced road-surface conditions. In this paper, we introduce a lightweight semantic segmentation algorithm that can recognize the area with road-surface damage through images at high speed to prevent occurrence of such accidents. To test the algorithm, an experiment was conducted in which more than 1,500 training data and 150 validation data, including road-surface damage, were newly created. By using the data, we propose a new deep neural network composed of only encoder type, unlike the auto-encoding type consisting of encoder and decoder. To evaluate the performance of the proposed algorithm, we considered four metrics of the accuracy and two metrics of the speed. Unlike the conventional method, this deep neural network method shows improvement in all of the accuracy index, a 85.7% decrease in parameters, and an 6.1% increase in computational speed. The application of such a high-speed algorithm is expected to improve safety in personal transportation.