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http://dx.doi.org/10.23087/jkicsp.2022.23.3.008

Implementation of AI-based Object Recognition Model for Improving Driving Safety of Electric Mobility Aids  

Je-Seung Woo (Kyeong Seong Technology Co.LTD)
Sun-Gi Hong (Kyeong Seong Technology Co.LTD)
Jun-Mo Park (Dept. of Digital Healthcare, Yonsei University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.23, no.3, 2022 , pp. 166-172 More about this Journal
Abstract
In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.
Keywords
Electric Mobility Aids; Transportation handicapped; Object Recognition; Deep learning; Detectron2;
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Times Cited By KSCI : 3  (Citation Analysis)
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