Visual Remote Monitoring and Control System for Rod Braking on Hot Rolling Mills. Starostenko, O., Trygub, I. G., Cruz-Perez, C., Alarcon-Aquino, V., & Potap, O. E. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10267 LNCS, pages 297–307. 2017. Paper doi abstract bibtex In steel production the finishing process on hot rolling mill includes a set of essential operations managed by complex control mechanical, electrical and hydraulic equipment. However, accuracy of mill automating mechanisms and sensors is still low due to hot hostile environment with strong vibration and shock. The proposed solution is a computer vision application that exploits morphological filtering and discontinuity masks for detection and separation of rods on rolling mill and provides fast recognition and tracking rod front ends during their deceleration on cooler. The proposed algorithm has been implemented and evaluated in real time conditions achieving precision of rod front end recognition in range of 90–98% on artificial and daylight illumination, respectively.
@incollection{starostenko_visual_2017,
title = {Visual {Remote} {Monitoring} and {Control} {System} for {Rod} {Braking} on {Hot} {Rolling} {Mills}},
volume = {10267 LNCS},
copyright = {All rights reserved},
url = {https://link.springer.com/10.1007/978-3-319-59226-8_29},
abstract = {In steel production the finishing process on hot rolling mill includes a set of essential operations managed by complex control mechanical, electrical and hydraulic equipment. However, accuracy of mill automating mechanisms and sensors is still low due to hot hostile environment with strong vibration and shock. The proposed solution is a computer vision application that exploits morphological filtering and discontinuity masks for detection and separation of rods on rolling mill and provides fast recognition and tracking rod front ends during their deceleration on cooler. The proposed algorithm has been implemented and evaluated in real time conditions achieving precision of rod front end recognition in range of 90–98\% on artificial and daylight illumination, respectively.},
booktitle = {Lecture {Notes} in {Computer} {Science} (including subseries {Lecture} {Notes} in {Artificial} {Intelligence} and {Lecture} {Notes} in {Bioinformatics})},
author = {Starostenko, Oleg and Trygub, Irina G. and Cruz-Perez, Claudia and Alarcon-Aquino, Vicente and Potap, Oleg E.},
year = {2017},
doi = {10.1007/978-3-319-59226-8_29},
keywords = {Computer vision, Morphological filtering, Pattern recognition and tracking, Steel manufacturing on rolling mill},
pages = {297--307},
}
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