Anomaly Detection in Television Digital Channel
Resumo
Detecting anomalies in industrial processes is a field in constant advancement. However, automating this task presents significant challenges due to the complexity of the problem. In this paper, Deep Learning techniques were employed to detect anomalies in video footage during digital channel testing of televisions on a production line. A 3D Convolutional Neural Network was trained on a dataset containing two classes of videos: those with simulated defects and those without defects. The resulting model achieved an accuracy of 98,45% with a processing speed of 648 FPS.
Palavras-chave:
Anomaly detection, Deep Learning, 3D Convolutional Neural Networks
Referências
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S. Aigner and M. Körner, “Futuregan: Anticipating the future frames of video sequences using spatio-temporal 3d convolutions in progressively growing gans,” arXiv preprint arXiv:1810.01325, 2018.
J. Villalba-Diez, D. Schmidt, R. Gevers, J. Ordieres-Meré, M. Buchwitz, and W. Wellbrock, “Deep learning for industrial computer vision quality control in the printing industry 4.0,” Sensors, vol. 19, no. 18, p. 3987, 2019.
L. A. da Silva, E. M. dos Santos, L. Aráujo, N. S. Freire, M. Vasconcelos, R. Giusti, D. Ferreira, A. S. Jesus, A. Pimentel, C. F. Cruz et al., “Spatio-temporal deep learning-based methods for defect detection: An industrial application study case,” Applied Sciences, vol. 11, no. 22, p. 10861, 2021.
A. Caggiano, J. Zhang, V. Alfieri, F. Caiazzo, R. X. Gao, and R. Teti, “Machine learning-based image processing for on-line defect recognition in additive manufacturing,” CIRP Annals, 2019. [Online]. Available: [link]
K. Imoto, T. Nakai, T. Ike, K. Haruki, and Y. Sato, “A cnn-based transfer learning method for defect classification in semiconductor manufacturing,” IEEE Transactions on Semiconductor Manufacturing, vol. PP, pp. 1–1, 09 2019.
V. Saligrama and Z. Chen, “Video anomaly detection based on local statistical aggregates,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2112–2119.
R. Nayak, U. C. Pati, and S. K. Das, “A comprehensive review on deep learning-based methods for video anomaly detection,” Image and Vision Computing, p. 104078, 2020.
W. Sultani, C. Chen, and M. Shah, “Real-world anomaly detection in surveillance videos,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
J. Ren, F. Xia, Y. Liu, and I. Lee, “Deep video anomaly detection: Opportunities and challenges,” in 2021 International Conference on Data Mining Workshops (ICDMW), 2021, pp. 959–966.
Y. Zhao, B. Deng, C. Shen, Y. Liu, H. Lu, and X.-S. Hua, “Spatio-temporal autoencoder for video anomaly detection,” 2017.
Z. Yang, J. Liu, Z. Wu, P. Wu, and X. Liu, “Video event restoration based on keyframes for video anomaly detection,” in IEEE/CVFCVPR, 2023, pp. 14 592–14 601.
W. Luo, W. Liu, D. Lian, and S. Gao, “Future frame prediction network for video anomaly detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7505–7520, 2022.
Y. Chang, Z. Tu, W. Xie, B. Luo, S. Zhang, H. Sui, and J. Yuan, “Video anomaly detection with spatio-temporal dissociation,” Pattern Recognition, vol. 122, p. 108213, 2022. [Online]. Available: [link]
A. F. Agarap, “Deep learning using rectified linear units (relu),” arXiv preprint arXiv:1803.08375, 2018.
“Keras layers globalaveragepooling2d: Large-scale machine learning on heterogeneous systems,” 2015. [Online]. Available: [link]
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatio-temporal features with 3d convolutional networks,” 2015. [Online]. Available: [link]
D. Kondratyuk, L. Yuan, Y. Li, L. Zhang, M. Tan, M. Brown, and B. Gong, “Movinets: Mobile video networks for efficient video recognition,” 2021. [Online]. Available: [link]
R. He, Y. Xiao, X. Lu, S. Zhang, and Y. Liu, “St-3dgmr: Spatio-temporal 3d grouped multiscale resnet network for region-based urban traffic flow prediction,” Information Sciences, vol. 624, pp. 68–93, 2023.
S. Aigner and M. Körner, “Futuregan: Anticipating the future frames of video sequences using spatio-temporal 3d convolutions in progressively growing gans,” arXiv preprint arXiv:1810.01325, 2018.
Publicado
06/11/2024
Como Citar
FABRICIO, Romulo; PIMENTEL, Agemilson; BELEM, Ruan; SOUSA, Anderson; MARTINHO, Laura; ARAÚJO, Leo; SILVA, Luan; SOUSA, Osmar.
Anomaly Detection in Television Digital Channel. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 19. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 13-18.
DOI: https://doi.org/10.5753/wvc.2024.34006.
