https://scholars.lib.ntu.edu.tw/handle/123456789/611423
標題: | Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures | 作者: | Wu R.-T. Singla A. Jahanshahi M.R. Bertino E. Ko B.J. Verma D. RIH-TENG WU |
關鍵字: | Convolution;Corrosion;Damage detection;Edge computing;Internet of things;Neural networks;Robots;Surface defects;Civil infrastructures;Condition assessments;Convolutional neural network;Health monitoring;Inspection robots;Internet of Things (IOT);Manual inspection;Resource efficiencies;Deep neural networks;artificial neural network;damage;detection method;infrastructure;robotics | 公開日期: | 2019 | 卷: | 34 | 期: | 9 | 起(迄)頁: | 774-789 | 來源出版物: | Computer-Aided Civil and Infrastructure Engineering | 摘要: | Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection?performance. ? 2019?Computer-Aided Civil and Infrastructure Engineering |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066137718&doi=10.1111%2fmice.12449&partnerID=40&md5=6f7ca53a7268d6d16b11bc0ae9a6eb01 https://scholars.lib.ntu.edu.tw/handle/123456789/611423 |
DOI: | 10.1111/mice.12449 |
顯示於: | 土木工程學系 |
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