Supervised Learning Plastic Defect Algorithm Detection
Keywords:
computer vision, passenger fatigue detection, Advanced Driver Assistance System
Abstract
The goal of this research is to develop a supervised learning algorithm able to detect the defects of plastic’s material. Finding patterns or examples in a dataset that differ from the norm is known as anomaly detection in plastic textures. Anomalies, in the context of plastic textures, can refer to imperfections’ deviations, or anomalies in the material that may have an impact on the final product's overall quality. Conventional anomaly detection techniques frequently rely on rule-based systems or manual examination, which can be laborious, subjective, and unable to identify small anomalies.
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References
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[2]. Yao H., Liu Y., Li X., You Z., Feng Y., Lu W., A Detection Method for Pavement Cracks Combining Object Detection and Attention Mechanism, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, p. 22179-22189, Nov. 2022.
[3]. Velasquez R. A., Vanessa J., Lara M., Automatic evaluation of cracks with semantic segmentation model with U-Net and an Efficient Net-b, IEEE XXX International Conference on Electronics, Electrical Engineering and Computing (INTERCON), p. 1-7, 2023.
[4]. Dinh T. H. et al., Toward Vision-Based Concrete Crack Detection: Automatic Simulation of Real -World Cracks, EEE Transactions on Instrumentation and Measurement, vol. 72, p. 1-15, art no. 5032015, 2023.
[5]. Park J., Chen Y. -C., Li Y. -J., Kitani K., Crack Detection and Refinement Via Deep Reinforcement Learning, IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 2021.
[6]. Akcay S., Abarghouei A. A., Breckon T. P., GANomaly: Semi-supervised anomaly detection via adversarial training, 2018.
[7]. Antipov G., Berrani S.-A., Ruchaud N., Dugelay J.-L., Learned vs. hand-crafted features for pedestrian gender recognition, Proceedings of the 23rd ACM International Conference on Multimedia, 2015.
[8]. Zhang J., Liang J., Zhang C., Zhao H., Scale invariant texture representation based on frequency decomposition and gradient orientation, Pattern Recognition Letters, vol. 51, p. 57-62, 2015.
[9]. Perepu P. K., Deep learning for detection of text polarity in natural scene images, Neurocomputing, vol. 431, 2021.
[10]. Zhu A., Wang G., Dong Y., Detecting natural scenes text via auto image partition, two-stage grouping and two-layer classification, Pattern Recognition Letters, vol. 67, Part 2, p. 153-162, 2015.
[11]. Do M. N., Vetterli M., Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance, IEEE Transactions on Image Processing, vol. 11, no. 2, p. 146-158, 2002.
[12]. Mathiassen J., Skavhaug A., Bo K., Texture Similarity Measure Using Kullback-Leibler Divergence between Gamma Distributions, ECCV 2002, LNCS 2352, p. 133-147, 2002.
[13]. Aiger D., Talbot H., The phase only transform for unsupervised surface defect detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 295-302, 2010.
[14]. Arivazhag S., Ganesan L., Bama S., Fault segmentation in fabric images using Gabor wavelet transform, Mach. Vis. Appl., vol. 16, p. 356-363, 2006.
[15]. Badrinarayanan V., Kendall A., Cipolla R., Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE transactions on pattern analysis and machine intelligence, 2017.
[16]. Maalej M., Cherif R., Yaddaden Y., Khoumsi A., Automatic Crack Detection on Concrete Structure Using a Deep Convolutional Neural Network and Transfer Learning, 2nd International Conference on Advanced Electrical Engineering (ICAEE), p. 1-6, 2022.
Published
2023-12-15
How to Cite
1.
MARIN FB, MARIN M. Supervised Learning Plastic Defect Algorithm Detection. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Dec.2023 [cited 30Oct.2024];46(4):89-2. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/6508
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