Research and Analysis of Image-based Food Identification Technology in Complex Scenarios

Abstract

Food image recognition, as a vital branch of finegrained visual analysis, holds significant promise for revolutionizing health management and the food industry. While deep learning models have achieved remarkable accuracy in controlled settings, their performance often degrades in real-world environments due to complex challenges. This paper presents a comprehensive review of food image recognition under these complex scenarios. This paper systematically analyzes the primary obstacles, including environmental disturbances (e.g., lighting and background variations), perspective and structural changes (e.g., occlusion and viewpoint diversity), intrinsic food variations (e.g., non-rigid deformation and inter-class similarity), and stringent system constraints (e.g., realtime and computational limits). Correspondingly, this paper surveys and discusses representative technical solutions, such as attention mechanisms, multimodal fusion, geometric transformation, and lightweight network architectures, highlighting their strengths and limitations. The review concludes that while existing methods have made substantial progress, critical issues like the accuracyefficiency trade-off, limited model generalization, and inadequate robustness in dynamic extremes remain unresolved. Future research should prioritize enhancing model adaptability in open environments, integrating semantic reasoning with perception, and developing comprehensive evaluation benchmarks to bridge the gap between laboratory research and practical deployment.