To improve automation, increase efficiency, and maintain high quality in the production of steel, applying modern machine learning techniques to help detect steel defects has been the research focus in the steel indus...
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ISBN:
(纸本)9781450376822
To improve automation, increase efficiency, and maintain high quality in the production of steel, applying modern machine learning techniques to help detect steel defects has been the research focus in the steel industry, since an unprecedented revolution in image semantic segmentation has been witnessed in the past few years. In the traditional production process of steel materials, localizing and classifying surface defects manually on a steel sheet is inefficient and error-prone. Therefore, it's a key challenge to achieve automated detection of steel surface defects in image pixel level, leaving an urgent and critical issue to be addressed. In this paper, to accomplish this crucial task, we apply a series of machine learning algorithms of real-time semantic segmentation, utilizing neuralnetworks with encoder-decoder architectures based on Unet and feature pyramid network (FPN). The image dataset of steel defects is provided by Severstal, the largest steel company in Russia, through a featured code competition in the Kaggle community. The results show that the ensemble algorithm of several neuralnetworks with encoder-decoder architectures has a decent performance regarding both time cost and segmentation accuracy. Our machine learning algorithms achieve dice coefficients over 0.915 and 0.905 at a speed of over 1.5 images per second on the public test set and private test set on the Kaggle platform, respectively, which locates at the top 2% among all teams in the competition.
Visual and thermal cameras have large modality gap while providing different information of the same scene. The spectral mapping of thermal imagery into color imagery is a challenging task due to inherit nonlinear rel...
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ISBN:
(纸本)9781538635643
Visual and thermal cameras have large modality gap while providing different information of the same scene. The spectral mapping of thermal imagery into color imagery is a challenging task due to inherit nonlinear relationship. This paper deals with the automatic colorization of thermal imagery into color images using deep encoder-decoder convolutional neuralnetwork architecture. The presented approach is trained and evaluated on an online thermal/color dataset, where the network is trained on the spectral mapping of thermal to color images. The training is automatic without requiring user intervention. Extensive experimentation is carried out on the online available dataset, demonstrating consistent and harmonious appearance of generated colorized images from input thermal images.
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