Bat-inspired temporal audiovisual fusion neural network in adverse weather conditions악천후 환경에서도 견고한 박쥐 모방 시청각 융합 신경망 개발

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This paper deals with a neural network that mimics a bat and provides 3D information by tracking a moving object even in severe weather such as sudden fog or rain. In the case of an ultrasonic sensor, it is possible to obtain the location of an object because it is robust even in bad weather, but it is impossible to accurately predict the size of the object. In contrast, a visual sensor such as a camera can obtain the location and size of an object, but has a disadvantage in that it does not operate properly in harsh environment. We implemented a network that can provide 3D information of objects even in bad weather conditions by mapping information obtained from images and ultrasound. We demonstrated the performance of the network through intersection over union (IoU) values, and these experimental results showed that objects can be tracked even in severe weather through the mutual complement of ultrasound and camera sensors.
Advisors
Bae, Hyeon-Minresearcher배현민researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2023
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2023.2,[iv, 23 p. :]

Keywords

Bat▼aUltrasound▼aImage▼aNeural network▼aSevere weather; 박쥐▼a초음파▼a이미지▼a뉴럴 네트워크▼a악천후

URI
http://hdl.handle.net/10203/309929
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032926&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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