Research on Both the Classification and Quality Control Methods of the Car Seat Backrest Based on Machine Vision

Sun, Shengnan; Jian-ping, Huang; Zhu, Juan; Yu, Yan; Zheng, Luyao

doi:10.1155/2022/3106313

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Some kernels captured the differences between samples, while others could not. Each convolution kernel extracted characteristics of interest from a specific angle, consistent with existing research [48] in image processing. The shapes of the convolutional kernels with different kernel sizes are illustrated in Figure 7.…”

Section: Visualization Of Features Extracted By the Cnn Networkmentioning

confidence: 83%

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Yao

2023

Remote Sensing

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Modeling the quantitative relationship between target components and measured spectral information is an essential part of laser-induced breakdown spectroscopy (LIBS) analysis. However, many traditional multivariate analysis algorithms must reduce the spectral dimension or extract the characteristic spectral lines in advance, which may result in information loss and reduced accuracy. Indeed, improving the precision and interpretability of LIBS quantitative analysis is a critical challenge in Mars exploration. To solve this problem, this paper proposes an end-to-end lightweight quantitative modeling framework based on ensemble convolutional neural networks (ECNNs). This method eliminates the need for dimensionality reduction of the raw spectrum along with other pre-processing operations. We used the ChemCam calibration dataset as an example to verify the effectiveness of the proposed approach. Compared with partial least squares regression (a linear method) and extreme learning machine (a nonlinear method), our proposed method resulted in a lower root-mean-square error for major element prediction (54% and 73% lower, respectively) and was more stable. We also delved into the internal learning mechanism of the deep CNN model to understand how it hierarchically extracts spectral information features. The experimental results demonstrate that the easy-to-use ECNN-based regression model achieves excellent prediction performance while maintaining interpretability.

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Section: Visualization Of Features Extracted By the Cnn Networkmentioning

confidence: 83%

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Yao

2023

Remote Sensing

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“…Detection of such faults helps not only with the correction of the error as soon as it occurs, but its classification can diagnose its exact cause, thus locating the machine failure that provoked it [12]. Some examples include the classification of car seat backrests through the combination of speeded up robust features (SURF) and a convolutional neural network (CNN) by Sun et al [13], and the inspection of defects such as cracks, tears or unwanted inclusions, and other elements, via region based CNN (R-CNN) with a modified stochastic gradient descent with momentum (SGDM) by Kuric et al [14]. As an example of fault detection done on welding and solder joints, Pei and Chen [15] suggest an inspection on door panels through a machine learning algorithm combined with template matching (based on the Canny edge detector and sequential similarity detection) for the former and the Hough transform and image segmentation for the latter.…”

Section: State-of-the-artmentioning

confidence: 99%

Hybrid Quality Inspection for the Automotive Industry: Replacing the Paper-Based Conformity List through Semi-Supervised Object Detection and Simulated Data

et al. 2022

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The still prevalent use of paper conformity lists in the automotive industry has a serious negative impact on the performance of quality control inspectors. We propose instead a hybrid quality inspection system, where we combine automated detection with human feedback, to increase worker performance by reducing mental and physical fatigue, and the adaptability and responsiveness of the assembly line to change. The system integrates the hierarchical automatic detection of the non-conforming vehicle parts and information visualization on a wearable device to present the results to the factory worker and obtain human confirmation. Besides designing a novel 3D vehicle generator to create a digital representation of the non conformity list and to collect automatically annotated training data, we apply and aggregate in a novel way state-of-the-art domain adaptation and pseudo labeling methods to our real application scenario, in order to bridge the gap between the labeled data generated by the vehicle generator and the real unlabeled data collected on the factory floor. This methodology allows us to obtain, without any manual annotation of the real dataset, an example-based F1 score of 0.565 in an unconstrained scenario and 0.601 in a fixed camera setup (improvements of 11 and 14.6 percentage points, respectively, over a baseline trained with purely simulated data). Feedback obtained from factory workers highlighted the usefulness of the proposed solution, and showed that a truly hybrid assembly line, where machine and human work in symbiosis, increases both efficiency and accuracy in automotive quality control.

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Research on Both the Classification and Quality Control Methods of the Car Seat Backrest Based on Machine Vision

Cited by 2 publications

References 13 publications

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Hybrid Quality Inspection for the Automotive Industry: Replacing the Paper-Based Conformity List through Semi-Supervised Object Detection and Simulated Data

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