Intelligent Target Recognition Technology Empowers the Improvement of Urban Public Safety Governance Capabilities

Abstract

The governance of urban public safety faces the dilemma of complex risks and insufficient effectiveness of traditional methods. Intelligent target recognition technology provides core support for solving this problem. This paper analyzes the multi-dimensional mechanism that this technology enables urban public security governance, and points out that it relies on convolutional neural network (CNN), transformer architecture and edge computing to build three mechanisms, namely, real-time accurate perception, active risk early warning, and cross sectoral collaborative decision-making, to promote the transformation of governance mode from passive response to active prevention and control. At the same time, this article reveals the four constraints faced by technology in practical implementation, including technological robustness, data privacy, institutional ethics, and organizational capacity. Based on this, we propose countermeasures and suggestions to promote model optimization and multimodal integration, improve regulatory standards and ethical review, strengthen data sharing and business process reengineering, and look forward to future research directions such as continuous learning, explainable AI, and privacy computing, providing a systematic reference for improving the efficiency of urban security governance.