图像识别中人工智能模型的性能评估与改进

Performance Evaluation and Improvement of Artificial Intelligence Models in Image Recognition

摘要: 明确图像识别领域人工智能模型性能评估的核心价值，梳理现有评估体系不足，可为模型改进提供理论与实践指引。采用文献梳理与逻辑分析相结合的方法，系统剖析主流评估指标、常用评估方法及典型数据集，归纳复杂场景下模型的性能短板并提出改进策略。结果表明，准确率、精确率等核心指标及单一指标评估、跨数据集验证等方法各有优劣；模型在光照变化、姿态差异、遮挡干扰、小样本数据等场景中存在明显性能瓶颈；数据增强、模型结构优化、迁移学习与多模型融合可有效提升模型性能。结论指出，需构建多维度综合评估体系，从数据、结构、算法多方面协同推进模型改进，以增强其复杂场景下的泛化与鲁棒性，助力图像识别技术实用化发展。

Abstract: Clarifying the core value of performance evaluation of artificial intelligence (AI) models in the field of image recognition and sorting out the deficiencies of existing evaluation systems can provide theoretical and practical guidance for model improvement. By adopting a combination of literature review and logical analysis, this study systematically analyzes mainstream evaluation metrics, common evaluation methods and typical datasets, summarizes the performance shortcomings of models in complex scenarios, and proposes improvement strategies. The results show that core metrics such as accuracy and precision, as well as methods including single-metric evaluation and cross-dataset validation, each have their own advantages and disadvantages; models have obvious performance bottlenecks in scenarios such as illumination changes, pose differences, occlusion interference, and small-sample data; data augmentation, model structure optimization, transfer learning, and multi-model fusion can effectively improve model performance. The conclusion points out that it is necessary to construct a multi-dimensional comprehensive evaluation system and promote model improvement collaboratively from multiple aspects of data, structure, and algorithms to enhance their generalization and robustness in complex scenarios, thereby facilitating the practical development of image recognition technology.