Smart Analysis of Campus Surveillance Based on Image Semantic Segmentation: Applications in Educational Management

In the context of digital campus development, traditional manual surveillance methods are increasingly inadequate due to their low efficiency and limited information processing capabilities.These limitations hinder the fulfillment of modern educational management demands in areas such as security control, teaching optimization, and logistical support.Image semantic segmentation technology, through pixel-level semantic understanding, provides critical support for the accurate identification of people, objects, and environments in campus surveillance scenarios.However, current research faces two key limitations: (1) insufficient segmentation accuracy for small or overlapping objects in complex campus environments; and (2) a lack of deep integration between technical applications and the practical needs of educational management, with no comprehensive application framework encompassing security management, teaching analysis, and logistical coordination.To address these challenges, this study focuses on two main aspects: first, it designs and optimizes an image semantic segmentation model tailored to the complexities of campus monitoring, with an emphasis on improving the recognition accuracy of small and overlapping targets; second, it explores the potential applications of this technology in core areas such as campus security alert systems, student behavior analysis, and instructional resource scheduling.Based on this, a deeply integrated framework is proposed that aligns technical models with application scenarios and management needs.The findings are expected to provide technical solutions for the intelligent upgrade of campus surveillance systems, promote the deep integration of image semantic segmentation with educational management processes, and contribute to more precise and efficient campus administration.