Application of lightweight target recognition models in edge computing

Abstract

Edge computing environments place high demands on the design of target recognition models, requiring efficiency, real-time performance, and resource sensitivity. Lightweight target recognition models, with their advantages of low computational overhead and flexible deployment, are gradually becoming a core technology for edge intelligent applications. This paper focuses on the implementation mechanism, optimization path, and typical applications of lightweight models in edge computing, aiming to explore the application value of model compression, architecture optimization, and collaborative computing strategies in real-world scenarios, and to promote the deep integration of target recognition and edge intelligence.