Design of the Automated Calibration Process for an Experimental Laser Inspection Stand

Klarák, Jaromír; Andok, Robert; Hricko, Jaroslav; Klačková, Ivana; Tsai, Hsiang‐Lin

doi:10.3390/s22145306

“…Similarly, in the context of public health measures like social distancing and face mask detection during pandemics [39], YOLO models provided essential support in enforcing health regulations. In industrial applications, YOLO aids in surface inspection processes to detect defects and anomalies [40,41], thereby ensuring quality control in manufacturing and production.…”

Section: You Only Look Once Approachmentioning

confidence: 99%

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Sapkota,

Qureshi,

Flores-Calero

et al. 2024

Preprint

5

0

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This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

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“…Similarly, in the context of public health measures like social distancing and face mask detection during pandemics [45,46], YOLO models provided essential support in enforcing health regulations. In industrial applications, YOLO aids in surface inspection processes to detect defects and anomalies [47,48], ensuring quality control in manufacturing and production.…”

Section: You Only Look Once Approachmentioning

confidence: 99%

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Sapkota,

Qureshi,

Flores-Calero

et al. 2024

Preprint

0

View full text Add to dashboard Cite

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

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“…The first step is the scanning procedure, which consist of a calibration process in the way of scanning the rotary shaft and removing the first harmonic component as eccentricity of assembling the rotary shaft to the rotary axis. This work is described in [ 37 ]. This way, the processed data are obtained, decrypted, and normalized to continuous values from 0 to 1, the most suitable for the training process.…”

Section: Point Cloud Reconstruction Based On 2d Convolutionmentioning

confidence: 99%

“…This was reflected in the sinusoidal display of the scanned object. The process of compensation and removal of this harmonic component is described in [ 37 ] using a two-step approach, where in the first step, the first harmonic component with a phase shift was identified based on the Fourier transform. For the sake of accuracy, this method was supplemented with a custom method for correcting the phase shift and the frequency of the first harmonic component in the data.…”

Section: Point Cloud Reconstruction Based On 2d Convolutionmentioning

confidence: 99%

Autoencoders Based on 2D Convolution Implemented for Reconstruction Point Clouds from Line Laser Sensors

Klarák

¹

,

Klačková

²

,

Andok

³

et al. 2023

Sensors

Self Cite

0

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Gradual development is moving from standard visual content in the form of 2D data to the area of 3D data, such as points scanned by laser sensors on various surfaces. An effort in the field of autoencoders is to reconstruct the input data based on a trained neural network. For 3D data, this task is more complicated due to the demands for more accurate point reconstruction than for standard 2D data. The main difference is in shifting from discrete values in the form of pixels to continuous values obtained by highly accurate laser sensors. This work describes the applicability of autoencoders based on 2D convolutions for 3D data reconstruction. The described work demonstrates various autoencoder architectures. The reached training accuracies are in the range from 0.9447 to 0.9807. The obtained values of the mean square error (MSE) are in the range from 0.059413 to 0.015829 mm. They are close to resolution in the Z axis of the laser sensor, which is 0.012 mm. The improvement of reconstruction abilities is reached by extracting values in the Z axis and defining nominal coordinates of points for the X and Y axes, where the structural similarity metric value is improved from 0.907864 to 0.993680 for validation data.

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Design of the Automated Calibration Process for an Experimental Laser Inspection Stand

Cited by 10 publications

References 22 publications

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Autoencoders Based on 2D Convolution Implemented for Reconstruction Point Clouds from Line Laser Sensors

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