Browsing by Author "Hu, J. S."

Now showing 1 - 3 of 3

Defect Detection on Randomly Textured Surfaces by Convolutional Neural Networks
(2018-01-01)
Jung, S. Y.
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Tsai, Y. H.
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Chiu, W. Y.
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Hu, J. S.
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Sun, C. T.
Automatically detecting the defects on the randomly textured surfaces for industrial purpose is a demanding procedure due to the ambiguity between defects and textures, lack of defect-labeled data and the must-have extreme accuracy. In this paper we proposed a procedure as the beginning of automating the defect detection on woods with randomly textured surfaces by employing 3 different architectures of convolutional neural networks. The deep convolutional neural network resulted in 99.80% accuracy, discriminating among normal wood and the other 4 types of defects images. The models were evaluated and understood by visualizing the saliency maps. The results from our work implies that other industrial images with defects on randomly textured surfaces may apply the similar procedures to accelerate the automating of defect detection and progressing of industry 4.0.
EMWF for Flexible Automation and Assistive Devices
(2009-01-01)
Chou, T. S.
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Wang, Y. C.
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Liu, J. W. S.
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Chang, S. Y.
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Lu, Y. F.
This paper describes an embedded workflow framework (EMWF) that enables flexible personal and home automation and assistive devices and service and social robots (collectively referred to as SISARL) to be built on workflow architecture. The process definition language supported by EMWF is called SISARL-XPDL. It consists of a subset of the WfMC standard XML Process Definition Language (XPDL) 2.0, together with elements that implement common mechanisms for robot behavior coordination. EMWF provides workflow engines for Linux and Windows CE platforms. The engines are written in C in order to keep their memory footprint and runtime overhead small. Performance data show that the overheads introduced by the engine and workflow data are tolerable for most SISARL devices.
Industrial Anomaly Detection and One-class Classification using Generative Adversarial Networks
(2018-01-01)
Lai, Y. T. K.
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Hu, J. S.
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Tsai, Y. H.
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Chiu, W. Y.
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資訊工程學系
Industrial image datasets for quality inspection are mostly sparse in defects. It is then hard for both automated optical inspection (AOI) machines and simple neural network classifiers to inspect all defects effectively. In this work, we develop a novel framework for industrial anomaly detection in one-class classification manner, which utilized pre-trained generative adversarial networks (GANs) as the rule of thumb to perform anomaly detection. Our results show that GANs are able to capture arbitrary and structural industrial images and can effectively discern defects when the query images are defective.