Integrating artificial intelligence into modern society is profoundly transformative, significantly enhancing productivity by streamlining various daily tasks. AI-driven recognition systems provide notable advantages in the food sector, including improved nutrient tracking, tackling food waste, and boosting food production and consumption efficiency. Accurate food classification is a crucial initial step in utilizing advanced AI models, as the effectiveness of this process directly influences the success of subsequent operations; therefore, achieving high accuracy at a reasonable speed is essential. Despite existing research efforts, a gap persists in improving performance while ensuring rapid processing times, prompting researchers to pursue cost-effective and precise models. This study addresses this gap by employing the state-of-the-art EfficientNetB7 architecture, enhanced through transfer learning, data augmentation, and the CBAM attention module. This methodology results in a robust model that surpasses previous studies in accuracy while maintaining rapid processing suitable for real-world applications. The Food11 dataset from Kaggle was utilized, comprising 16643 imbalanced images across 11 diverse classes with significant intra-category diversities and inter-category similarities. Furthermore, the proposed methodology, bolstered by various deep learning techniques, consistently achieves an impressive average accuracy of 96.40%. Notably, it can classify over 60 images within one second during inference on unseen data, demonstrating its ability to deliver high accuracy promptly. This underscores its potential for practical applications in accurate food classification and enhancing efficiency in subsequent processes.

翻译：将人工智能融入现代社会具有深刻的变革性，通过简化各类日常任务显著提升了生产力。人工智能驱动的识别系统在食品领域展现出显著优势，包括改善营养追踪、应对食物浪费以及提升食品生产与消费效率。精确的食物分类是利用先进人工智能模型的关键初始步骤，因为该过程的有效性直接影响后续操作的成功；因此，在合理速度下实现高精度至关重要。尽管已有研究努力，但在确保快速处理时间的同时提升性能方面仍存在差距，这促使研究者追求经济高效且精确的模型。本研究通过采用最先进的EfficientNetB7架构，并借助迁移学习、数据增强以及CBAM注意力模块进行增强，以弥补这一差距。该方法构建了一个鲁棒的模型，在保持适用于实际应用的快速处理能力的同时，其准确率超越了先前的研究。实验使用了来自Kaggle的Food11数据集，该数据集包含16643张不平衡图像，涵盖11个不同类别，具有显著的类内多样性与类间相似性。此外，所提出的方法在多种深度学习技术的支持下，持续实现了96.40%的平均准确率，表现令人瞩目。值得注意的是，在未见数据上进行推理时，该方法能在一秒内分类超过60张图像，证明了其及时提供高精度分类的能力。这凸显了其在精确食物分类及提升后续流程效率方面实际应用的潜力。