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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chuang, Jen-HuiSubject: search.filters.subject.Deep learning

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    DEEP LEARNING-BASED OBSTACLE DETECTION AND DEPTH ESTIMATION
    (2019-01-01)
    Hsieh, Yi-Yu
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    Lin, Wei-Yu
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    Li, Dong-Lin
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    Chuang, Jen-Hui
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    資訊工程學系
    This paper proposed a modified YOLOv3 which has an extra object depth prediction module for obstacle detection and avoidance. We use a pre-processed KITTI dataset to train the proposed, unified model for (i) object detection and (ii) depth prediction and use the AirSim flight simulator to generate synthetic aerial images to verify that our model can be applied in different data domains. Experimental results show that the proposed model compares favorably with other depth map prediction methods in terms of accuracy in the prediction of object depth for pre-processed KITTI dataset, while the unified approach can actually improve both (i) and (ii) at the same time.
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    DEEP LEARNING-BASED HUMAN ACTIVITY ANALYSIS FOR AERIAL IMAGES
    (2017-01-01)
    Wang, Han-Yang
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    Chang, Ya-Ching
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    Hsieh, Yi-Yu
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    Chen, Hua-Tsung
    ;
    Chuang, Jen-Hui
    Due to the advantages of high mobility and the ability to fly in the sky, drone has inspired more and more applications in recent years. On the other hand, deep learning-based human activity analysis is an important research topic in security surveillance; however, there are few research works on such analysis with aerial images so far. Because of perspective projection, people in aerial images look tilted, which would degrade the performance of human activity analysis. In order to cope with the issue of perspective projection for aerial images, we modify the CNN architecture of a state-ofthe-art object detection method, YOLOv2 [12], and build an aerial image dataset with a drone for new model training. Finally, a post -processing method is proposed to classify the pose of a detected person as normal or abnormal, so that the task of human activity analysis with aerial images can be accomplished.