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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Semantic Image Segmentation in Similar Fusion Background for Self-driving Vehicles

ChienHsiang Wu, TzuChi Tai, and ChinFeng Lai

(Received May 6, 2021; Accepted December 1, 2021)

Keywords: image segmentation, image enhancement, self-driving vehicle, similar fusion background image

Self-driving vehicles have become more and more popular in recent years. Because of this, the information fusion sensing method using radar and cameras has been widely adopted in vehicles. We use the vehicle camera sensor and robust image segmentation technology to solve its inherent shortcomings. These images used for the image segmentation are obtained under adverse weather conditions, or the image object's color and texture resemble the background. For such images, using the convolutional layers model for image segmentation as a feature extraction method usually leads to error. Any highly robust algorithms for image enhancement for self-driving operation will help alleviate problems related to driving safety. To ensure that the final image segmentation achieves the desired effect and reduces the error rate, we propose a new segmentation-twice method, which correctly classifies the object's label. The test results of the simulation described in this paper show that this experiment correctly classifies the object's label. It can provide accurate environmental perception information for autonomous vehicles, improve the segmentation effect of similar fusion background images and reduce the error rate.

Corresponding author: ChinFeng Lai




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