Research on Anomaly Detection for Intelligent Inspection Robot Based on Computer Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69457-8_64
Reference9 articles.
1. Mu, H., Sun, R., Wang, M., et al.: Spatio-temporal graph-based CNNs for anomaly detection in weakly-labeled videos. Inf. Process. Manag. 59, 102983 (2022)
2. Cao, C., Zhang, X., Zhang, S., et al.: Adaptive graph convolutional networks for weakly supervised anomaly detection in videos. IEEE Signal Process. Lett. 29, 2497 (2022)
3. Dhondkar, S., Khare, M., Kumar, P.: Anomaly detection in image sequences using weakly supervised learning. In: Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2022, pp. 443–455. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-6004-8_36
4. Deshpande, K., Singh Punn, N., Sonbhadra, S.K., et al.: Anomaly detection in surveillance videos using transformer based attention model. In: International Conference on Neural Information Processing, pp. 199–211. Springer, Singapore (2022). https://doi.org/10.1007/978-981-99-1648-1_17
5. Kang, Y., Chen, G., Wang, H., et al.: Dual-input anomaly detection method based on deep reinforcement learning. Struct. Health Monit. 23(3), 1578–1591 (2024)
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