Polarization Image Laser Line Extraction Methods for Reflective Metal Surfaces

Marco-Rider, Jaime; Cibicik, Andrej; Egeland, Olav

doi:10.1109/jsen.2022.3194258

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…Therefore, feature extraction algorithms should be able to handle some data noise robustly. It is noted that the majority of methods are derived for processing the image data obtained directly from the camera of the structured light sensor [6], [9], [11]- [14], while others process the triangulated points, which is the final sensor output [3], [5], [7], [15]. The latter methods can be used with sensors where access to the raw image data is limited.…”

Section: Introductionmentioning

confidence: 99%

CNN-based Feature Extraction for Robotic Laser Scanning of Weld Grooves in Tubular T-joints

Mjolhus

Cibicik

Njaastad

et al. 2022

2022 Sixth IEEE International Conference on Robotic Computing (IRC)

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This paper presents an algorithm for feature point extraction from scanning data of large tubular T-joints (a subtype of a TKY joint). Extracting such feature points is a vital step for robot path generation in robotic welding. Therefore, fast and reliable feature point extraction is necessary for developing adaptive robotic welding solutions. The algorithm is based on a Convolutional Neural Network (CNN) for detecting feature points in a scanned weld groove, where the scans are done using a laser profile scanner. To facilitate fast and efficient training, we propose a methodology for generating synthetic training data in the computer graphics software Blender using realistic physical properties of objects. Further, an iterative feature point correction procedure is implemented to improve initial feature point results. The algorithm's performance was validated using a real-world dataset acquired from a large tubular T-joint.

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Section: Introductionmentioning

confidence: 99%

CNN-based Feature Extraction for Robotic Laser Scanning of Weld Grooves in Tubular T-joints

Mjolhus

Cibicik

Njaastad

et al. 2022

2022 Sixth IEEE International Conference on Robotic Computing (IRC)

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Polarization image demosaicing and RGB image enhancement for a color polarization sparse focal plane array

Liu¹,

Jin²,

Hao³

et al. 2023

Opt. Express

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The color division of focal plane (DoFP) polarization sensor structure mostly uses Bayer filter and polarization filter superimposed on each other, which makes the polarization imaging unsatisfactory in terms of photon transmission rate and information fidelity. In order to obtain high-resolution polarization images and high-quality RGB images simultaneously, we simulate a sparse division of focal plane polarization sensor structure, and seek a sweet spot of the simultaneous distribution of the Bayer filter and the polarization filters to obtain both high-resolution polarization images and high-quality RGB images. In addition, From the perspective of sparse polarization sensor imaging, leaving aside the traditional idea of polarization intensity interpolation, we propose a new sparse Stokes vector completion method, in which the network structure avoids the introduction and amplification of noise during polarization information acquisition by mapping the S1 and S2 components directly. The sparsely polarimetric image demosaicing (Sparse-PDM) model is a progressive combined structure of RGB image artifact removal enhancement network and sparsely polarimetric image completion network, which aims to compensate sparsely polarimetric Stokes parameter images with the de-artifacts RGB image as a guide, thus achieving high-quality polarization information and RGB image acquisition. Qualitative and quantitative experimental results on both self-constructed and publicly available datasets prove the superiority of our method over state-of-the-art methods.

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Identification and Classification of Aluminum Scrap Grades Based on the Resnet18 Model

et al. 2022

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In order to reduce the elemental species produced in the recycling and melting of aluminum scrap and to improve the quality of pure aluminum and aluminum alloys, it is necessary to classify the different grades of aluminum scrap before melting. For the problem of classifying different grades of aluminum scrap, most existing studies are conducted using laser-induced breakdown spectroscopy for identification and classification, which requires a clean and flat metal surface and enormous equipment costs. In this study, we propose a new classification and identification method for different grades of aluminum scrap based on the ResNet18 network model, which improves the identification efficiency and reduces the equipment cost. The objects of this research are three grades of aluminum scrap: 1060, 5052, and 6061. The surface features of the three grades were compared using a machine vision algorithm; three different datasets, using RGB, HSV, and LBP, were built for comparison to find the best training dataset for subsequent datasets, and the hyperparameters of learning rate and batch size were tuned for the ResNet18 model. The results show that there was a differentiation threshold between different grades through the comparison of surface features; the ResNet18 network model trained the three datasets, and the results showed that RGB was the best dataset. With hyperparameter optimization of the ResNet18 model, the accuracy of final classification and recognition could reach 100% and effectively achieve the classification of different grades of aluminum scrap.

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Polarization Image Laser Line Extraction Methods for Reflective Metal Surfaces

Cited by 3 publications

References 55 publications

CNN-based Feature Extraction for Robotic Laser Scanning of Weld Grooves in Tubular T-joints

CNN-based Feature Extraction for Robotic Laser Scanning of Weld Grooves in Tubular T-joints

Polarization image demosaicing and RGB image enhancement for a color polarization sparse focal plane array

Identification and Classification of Aluminum Scrap Grades Based on the Resnet18 Model

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