Ladle pouring process parameter and quality estimation using mask r-cnn and contrast-limited adaptive histogram equalisation

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SUMMARY

    Deep learning is a sub-field of machine_learning in which multilayer neural_networks are utilised to interpret large and/ or complex data. Convolutional neural_networks (CNNs) are capable of learning spatial information, making them useful for visual recognition tasks including classification, localisation, detection, segmentation and tracking. Traditional artificial neural_networks (ANNs) have even been used to intelligently reschedule the steelmaking-continuous-casting production process. PANet (Path Aggregation Network) is a successor of Mask R-CNN won the MSCOCO challenge in 2017 and has shown improved performance over Mask R-CNN on the COCO dataset (0.42 segmentation . . .

     

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