Security Monitoring Image Recognition Technology Based on Deep Learning
DOI:
https://doi.org/10.53469/wjimt.2025.08(08).09Keywords:
Deep learning, Security monitoring, Image recognition, Facial recognition, Outlier detectionAbstract
The application of deep learning technology in security monitoring image recognition has significantly improved the intelligence and efficiency of the system. From fundamental principles to practical applications, deep learning has shown outstanding performance in image recognition and abnormal behavior detection, greatly improving the accuracy and real-time performance of face recognition and behavior analysis. However, there are still challenges in the application of technology, such as high demand for computing resources and strong dependence on data. These problems can be effectively solved through edge computing, transfer learning, multimodal fusion and other methods. In the future, breakthroughs in deep learning technology in fields such as quantum computing, multimodal learning, and biometric recognition will drive more intelligent and efficient security monitoring systems.
References
Q. Tian, D. Zou, Y. Han and X. Li, "A Business Intelligence Innovative Approach to Ad Recall: Cross-Attention Multi-Task Learning for Digital Advertising," 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shenzhen, China, 2025, pp. 1249-1253, doi: 10.1109/AINIT65432.2025.11035473.
Wang, Zhiyuan, et al. "An Empirical Study on the Design and Optimization of an AI-Enhanced Intelligent Financial Risk Control System in the Context of Multinational Supply Chains." (2025).
Xie, Y., Li, Z., Yin, Y., Wei, Z., Xu, G., & Luo, Y. (2024). Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification. Journal of Theory and Practice of Engineering Science, 4(02), 15–22. https://doi.org/10.53469/jtpes.2024.04(02).03
Chen, Yinda, et al. "Generative text-guided 3d vision-language pretraining for unified medical image segmentation." arXiv preprint arXiv:2306.04811 (2023).
Xu, Haoran. "CivicMorph: Generative Modeling for Public Space Form Development." (2025).
Tu, Tongwei. "ProtoMind: Modeling Driven NAS and SIP Message Sequence Modeling for Smart Regression Detection." (2025).
Xie, Minhui, and Boyan Liu. "InspectX: Optimizing Industrial Monitoring Systems via OpenCV and WebSocket for Real-Time Analysis." (2025).
Peng, Q., Planche, B., Gao, Z., Zheng, M., Choudhuri, A., Chen, T., Chen, C. and Wu, Z., 3D Vision-Language Gaussian Splatting. In The Thirteenth International Conference on Learning Representations.
Wang, Y. (2025). Efficient Adverse Event Forecasting in Clinical Trials via Transformer-Augmented Survival Analysis.
Liu, Jun, et al. "Toward adaptive large language models structured pruning via hybrid-grained weight importance assessment." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 18. 2025.
Zhou, Dianyi. "Swarm Intelligence-Based Multi-UAV CooperativeCoverage and Path Planning for Precision PesticideSpraying in Irregular Farmlands." (2025).
Tan, C., Gao, F., Song, C., Xu, M., Li, Y., & Ma, H. (2024). Highly Reliable CI-JSO based Densely Connected Convolutional Networks Using Transfer Learning for Fault Diagnosis.
Zhuang, R. (2025). Evolutionary Logic and Theoretical Construction of Real Estate Marketing Strategies under Digital Transformation. Economics and Management Innovation, 2(2), 117-124.
Han, X., & Dou, X. (2025). User recommendation method integrating hierarchical graph attention network with multimodal knowledge graph. Frontiers in Neurorobotics, 19, 1587973.
Yang, J. (2025, July). Identification Based on Prompt-Biomrc Model and Its Application in Intelligent Consultation. In Innovative Computing 2025, Volume 1: International Conference on Innovative Computing (Vol. 1440, p. 149). Springer Nature.