Food Category Recognition for Meal-Assistance Robots Using MEAL-YOLOv8

REN Shiyang; WU Weiming; HU Bingshan; YU Hongliu

doi:10.11907/rjdk.251103

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Food Category Recognition for Meal-Assistance Robots Using MEAL-YOLOv8

Artificial Intelligence | 更新时间：2026-04-30

- Food Category Recognition for Meal-Assistance Robots Using MEAL-YOLOv8
- Software Guide Vol. 25, Issue 4, Pages: 20-26(2026)
- 作者机构：
  
  上海理工大学智能康复工程研究院，上海 200093
- 作者简介：
- 基金信息：
- DOI：10.11907/rjdk.251103
  CLC： TP391
- Received：18 February 2025，
  
  Published：15 April 2026
- 稿件说明：
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任诗扬,吴伟铭,胡冰山等.基于MEAL-YOLOv8的助餐机器人食物种类识别[J].软件导刊,2026,25(04):20-26.

REN Shiyang,WU Weiming,HU Bingshan,et al.Food Category Recognition for Meal-Assistance Robots Using MEAL-YOLOv8[J].Software Guide,2026,25(04):20-26.
任诗扬,吴伟铭,胡冰山等.基于MEAL-YOLOv8的助餐机器人食物种类识别[J].软件导刊,2026,25(04):20-26. DOI： 10.11907/rjdk.251103.

REN Shiyang,WU Weiming,HU Bingshan,et al.Food Category Recognition for Meal-Assistance Robots Using MEAL-YOLOv8[J].Software Guide,2026,25(04):20-26. DOI： 10.11907/rjdk.251103.

摘要

助餐机器人在辅助老年人和上肢残疾人方面展现出巨大潜力。助餐机器人通常体积小、具备良好的移动便捷性，因此必须采用轻量化设计，在有限的空间内高效运作并节省能耗。为此，提出MEAL-YOLOv8轻量化视觉模型实现更高效的食物分类功能，结合了BRA与MSDA两种注意力机制，融合了YOLOv8与MobileNetV3的优点。在JETSON NANO B01平台上部署后，MEAL-YOLOv8模型大小仅为2.37 MB，内存占用为158 MB，分类准确率达到91.4%，具有高精度、低延迟的视觉识别能力，使助餐机器人能在资源受限的环境中高效运作，能实时、准确地识别食物。

Abstract

Catering robots have shown great potential in assisting the elderly and people with upper limb disabilities. Food assistance robots are usually small in size and require good convenience and mobility， so they must adopt lightweight design to operate efficiently and save energy in limited space. Therefore， a lightweight visual model of MEAL-YOLOv8 is proposed to achieve more efficient food classification function， combining BRA and MSDA attention mechanisms， and integrating the advantages of YOLOv8 and MobileNetV3. After deployment on the JETSON NANO B01 platform， the MEAL-YOLOv8 model has a size of only 2.37 MB， a memory usage of 158 MB， and a classification accuracy of 91.4%. It has high-precision and low latency visual recognition capabilities， allowing the food aid robot to operate efficiently in resource limited environments and recognize food in real-time and accurately.

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references

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