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- W2400231982 abstract "In this paper we present a comparison between standard computer vision techniques and Deep Learning approach for automatic metal corrosion (rust) detection. For the classic approach, a classification based on the number of pixels containing specific red components has been utilized. The code written in Python used OpenCV libraries to compute and categorize the images. For the Deep Learning approach, we chose Caffe, a powerful framework developed at “Berkeley Vision and Learning Center” (BVLC). The test has been performed by classifying images and calculating the total accuracy for the two different approaches." @default.
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- W2400231982 date "2016-05-21" @default.
- W2400231982 modified "2023-10-12" @default.
- W2400231982 title "Corrosion Detection Using A.I : A Comparison of Standard Computer Vision Techniques and Deep Learning Model" @default.
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- W2400231982 doi "https://doi.org/10.5121/csit.2016.60608" @default.
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