Positioning error estimation of steel strips in Steckel rolling process using digital image processing

Lemos, Aline de Faria; Silva, Leonardo Adolpho Rodrigues da; Furtado, Edgar Campos; Paula, Hélder de

doi:10.1109/ias.2017.8101825

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The realignment correction procedure requires a reaction time that might exceed the human capability, becoming susceptible to failure due to the high longitudinal speed of the strips. In addition, a manual command can lead to inadequate control, as it is an imprecise tool [9,11].…”

Section: Introductionmentioning

confidence: 99%

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Automatic monitoring of steel strip positioning error based on semantic segmentation

Lemos

Silva

Nagy

2020

Int J Adv Manuf Technol

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The misalignment of steel strips in relation to the roller table centerline still is an impairment for the rolling mill production lines. Nowadays, the strip position correction remains largely in the purview of human analysis, in which the strip steering is traditionally a semi-manual operation. Automating the alignment process could reduce the maintenance costs, damage to the plant, and prevent material losses. The first step into the automatization is to determine the strip position and its referred error. This study presents a method that employs semantic segmentation based on convolution neural networks to estimate steel strips positioning error from images of the process. Additionally, the system mitigates the influences of mechanical vibration on the images. The system performance was assessed by standard semantic segmentation evaluation metrics and in comparison with the dataset ground truth. The results showed that 97% of the estimated positioning errors are within a 2-pixel margin. The method demonstrated to be a robust real-time solution as the networks were trained from a set of low-resolution images acquired in a complex environment.

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Section: Introductionmentioning

confidence: 99%

“…This study was performed in a dataset with limited size. The second study applies morphological operations in processed images to calculate steel strip positioning error [9]. However, the work lacks validation from a ground truth set.…”

Section: Introductionmentioning

confidence: 99%

Automatic monitoring of steel strip positioning error based on semantic segmentation

Lemos

Silva

Nagy

2020

Int J Adv Manuf Technol

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“…A pixel to pixel XOR logical operator is applied to perform the comparison between the polygons image and the binary human hand image. It consists of an XOR operation between each pixel of the polygons image and its correspondent pixel in the hand image, similarly to methods present in the available literature (Bovik and Desai, 2000;Koukounis et al, 2011;Mookdarsanit et al, 2015;de Faria Lemos et al, 2017). An analogous procedure is illustrated in Fig.…”

Section: Pattern Recognition Modulementioning

confidence: 99%

A Markerless Joint Detection through a Hand Geometric Representation

Lemos

Vince

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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In several fields, such as man-machine interface and occupational therapy, the human hand-joint position is required. Traditional methods usually rely on image processing allied with marker placement, e.g. reflexive marker, which can be time-consuming and uncomfortable for the subject. For these reasons, scientific efforts are being made to create reliable and convenient joint tracking. This paper proposes a methodology that generates geometric figures to mimic the hand configuration. This process is made possible by an optimization algorithm, which finds the most suitable placement of these geometric shapes. One time the real hand and the created representation share similar features, the joints position can be estimated. Two optimization algorithms were employed: particle swarm optimization and genetic algorithm. In both cases, satisfactory results were obtained. Although, particle swarm optimization marginally outperformed the latter method.

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Semantic Segmentation Based on Convolution Neural Network for Steel Strip Position Estimation

Lemos

Nagy

2020

Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

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Positioning error estimation of steel strips in Steckel rolling process using digital image processing

Cited by 3 publications

References 15 publications

Automatic monitoring of steel strip positioning error based on semantic segmentation

Automatic monitoring of steel strip positioning error based on semantic segmentation

A Markerless Joint Detection through a Hand Geometric Representation

Semantic Segmentation Based on Convolution Neural Network for Steel Strip Position Estimation

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