S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Object Detection of Road Facilities Using YOLOv3 for High-definition Map Updates

Tae-Young Lee, Myeong-Hun Jeong, and Almirah Peter

(Received November 15, 2021; Accepted January 4, 2022)

Keywords: high-definition (HD) map, object detection, autonomous driving, deep learning, YOLOv3

Autonomous driving technology is based significantly on the fusion of high-definition (HD) maps and sensors. Therefore, the construction and update of HD maps must be emphasized to achieve full driving automation. Herein, a method is proposed to detect road facilities using object detection with images, particularly for HD map updates utilizing the You Only Look Once version 3 (YOLOv3) algorithm. The proposed approach, a deep-learning-based object detection method, utilizes transfer learning, which can detect objects in road facilities and record road sections that require maintenance. To test the effectiveness of the detection method, we analyze video footage captured in the Korean road environment. The experimental results show that this method achieves a mean average precision (mAP) of 58 and can update HD maps using a crowdsourcing framework.

Corresponding author: Myeong-Hun Jeong




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