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<p>With the changes of the times and the rapid development of fast food, obesity has become a common concern in modern society. It is important for modern people to consider how to effectively manage their diet. Taking fast-food restaurants as an example, this study proposes a numerical label counting method and builds a food recognition system based on deep-learning-based YOLO object detection technology, in which 16 classes of well-known fast-food restaurants are trained, with a total of 1836 multi-label photos. When the detector accuracy rate is IoU = 0.6, mAP can reach 93.29%. In addition, the numerical-label-based multi-count method proposed in this study can quickly search and count a large number of food objects with a time complexity of O(Nlog2N). In a 50-search experiment of 75 items, the numerical-label-based multi-count method can be up to 2.152 times faster than the text label counting method. Moreover, this study uses the Eigen-CAM method of xAI technology to further examine the correctness of the results predicted by the detection model through visualization, where the Eigen-CAM method is one of class activation map (CAM) techniques for analyzing classification problems in computer vision. Finally, users can use the Android-based APP built by this study to identify and count food, quickly query the calories and nutritional content of food, and record information related to food intake, so as to provide users with automated diet management.</p>

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Deep learning-based automatic food identification with numeric label

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月1日

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  • Yen-Chiu Chen,
  • Hao-Chun Chiang

摘要

With the changes of the times and the rapid development of fast food, obesity has become a common concern in modern society. It is important for modern people to consider how to effectively manage their diet. Taking fast-food restaurants as an example, this study proposes a numerical label counting method and builds a food recognition system based on deep-learning-based YOLO object detection technology, in which 16 classes of well-known fast-food restaurants are trained, with a total of 1836 multi-label photos. When the detector accuracy rate is IoU = 0.6, mAP can reach 93.29%. In addition, the numerical-label-based multi-count method proposed in this study can quickly search and count a large number of food objects with a time complexity of O(Nlog2N). In a 50-search experiment of 75 items, the numerical-label-based multi-count method can be up to 2.152 times faster than the text label counting method. Moreover, this study uses the Eigen-CAM method of xAI technology to further examine the correctness of the results predicted by the detection model through visualization, where the Eigen-CAM method is one of class activation map (CAM) techniques for analyzing classification problems in computer vision. Finally, users can use the Android-based APP built by this study to identify and count food, quickly query the calories and nutritional content of food, and record information related to food intake, so as to provide users with automated diet management.