Browsing by Author "Lu, Wei-Min"

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Vision-Based Landing System Design for a Small UAV
(2015)
Lin, Yu-Fu
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Lu, Wei-Min
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Chen, Kuan-Hung
;
Guo, Jiun-In
;
電子工程學系及電子研究所
This paper presents a vision-based landing system concept which could be implemented in a small UAV (Unmanned Aerial Vehicle). Vision-based object detection provides object position information of objects such as pedestrian or road, and those detection methods can also provide precise position during the landing stage of aircraft. Besides, the take off weight of a small UAV should be light in order to increase endurance. Vision-based sensor is much low-cost compared with other sensors such as RADAR (Radio Detection And Ranging), LiDAR (Light Detection And Ranging) or DGPS (differential GPS) module. However, vision-based object detection methods have several challenges such as weather conditions, low illumination capability, and high false detection rate in complicated environment. Accordingly, this paper presents a practical method that can conquers the above challenges for vision-based landing system for UAV. In addition to desktop simulation, we also realize the proposed method on a portable device.
Vision-Based Moving Objects Detection for Intelligent Automobiles and a Robustness Enhancing Method
(2014-01-01)
Ju, Ting-Fung
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Lu, Wei-Min
;
Chen, Kuan-Hung
;
Guo, Jiun-In
;
電子工程學系及電子研究所
This paper presents a vision-based moving objects detection work which attracts much attention in intelligent automobile applications recently. Vision-based objects detection provides object behavior information of objects and is an intuitive detection method similar to human visual perception. Besides, vision-based objects detection methods are much low-cost compared with detection methods such as RADAR (Radio Detection And Ranging), or LiDAR (Light Detection And Ranging). However, current vision-based objects detection methods still suffer from several challenges such as high false alarms and unstable detection rate which limit their value in practical applications. Accordingly, this paper presents a robustness enhancing method for vision-based moving objects detection.