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English(EN) Vision-Based Calorie Estimation for Bangladeshi Street Food: A Comparative Study of Detection and Regression Models

新系统利用人工智能模型估算孟加拉国街头食品热量

研究人员开发了一个基于视觉的系统,用于估算孟加拉国街头食品的热量含量,填补了当前以西方为中心的做法中的空白。该研究比较了五种物体检测和分割模型,其中YOLO11n取得了最高的检测性能。对于热量预测,一个随机森林回归模型利用YOLO11n提取的几何特征和一个孟加拉国5塔卡硬币作为比例参考,取得了最佳结果,平均绝对误差为5.68。 AI

影响 这项研究可以实现更准确的饮食监测,并为多样化的全球美食开发专门的移动健康应用程序。

排序理由 该集群包含一篇学术论文,详细介绍了针对特定应用的新方法和人工智能模型比较研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新系统利用人工智能模型估算孟加拉国街头食品热量

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该集群包含一篇学术论文,详细介绍了针对特定应用的新方法和人工智能模型比较研究。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Aparup Dhar, Antu Das, Pritom Barua, MD Tamim Hossain ·

    基于视觉的孟加拉国街头食品热量估算:检测与回归模型比较研究

    arXiv:2509.01415v2 Announce Type: replace Abstract: With obesity emerging as a major global health concern, accurate calorie estimation systems have become increasingly important for effective dietary management. Current vision-based approaches are inappropriate for Bangladeshi s…