[1]彭 耿,刘宁钟.基于注意力机制的食物识别与定位算法[J].计算机技术与发展,2022,32(11):121-126.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 018]
　PENG Geng,LIU Ning-zhong.Food Recognition and Location Algorithm Based on Attention Mechanism[J].,2022,32(11):121-126.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 018]
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基于注意力机制的食物识别与定位算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年11期
页码:
121-126
栏目:
人工智能
出版日期:
2022-11-10

文章信息/Info

Title:
Food Recognition and Location Algorithm Based on Attention Mechanism
文章编号:
1673-629X(2022)11-0121-06
作者:
彭 耿; 刘宁钟
南京航空航天大学 计算机科学与技术学院,江苏 南京 211106
Author(s):
PENG Geng; LIU Ning-zhong
School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
关键词:
食物识别与定位; 深度学习; 注意力机制; 特征融合; YOLO
Keywords:
food recognition and location; deep learning; attention mechanism; feature fusion; YOLO
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 11. 018
摘要:
随着食物检索、食物推荐和食物监测等应用需求的增加,食物的自动分析成为了研究的热点。 由于食物种类多,存在类间差异小、类内差异大、多尺度等特点,食物识别和定位的准确率一直不高。 并且目前很多研究,在食物分析任务中,推理速度慢,性能不佳。 针对这些问题,结合注意力机制,提出了一个更优的主干网络,能更好地提取食物细粒度特征。 同时对 Neck 部分进行研究,进行多尺度特征融合,提出了一种轻量级的端到端食物识别和定位框架 FFAM( FeatureFusion of Attention Mechanism) 。 在目前具有挑战性的公开数据集 UNIMIB2016 上的实验结果表明,该算法比目前的很多方法在精度上更具有优势,最终 mAP 达到了 94. 1% 。 由于得到的模型相比 YOLOv4 精度高且更小,在应对移动端、嵌入式设备中部署食物分析模型解决实际任务时,能有一个更好的性能表现。
Abstract:
With the increasing demand for applications such as food retrieval, food recommendation and food monitoring, automaticanalysis of food has become a hot research topic. The accuracy of food identification and localization has been low due to the largenumber of food types with small inter-class differences,large intra-class differences,and multiple scales. And many current studies,inthe food analysis task, have slow inference speed and poor performance. To address these problems, a better backbone network isproposed to extract food fine-grained features better by combining the attention mechanism. The Neck part is also investigated for multi-scale feature fusion,and a lightweight end-to-end food identification and localization framework FFAM is proposed. The experiments onthe current challenging public dataset UNIMIB2016 show that the proposed algorithm is more competitive than many current methods interms of accuracy,with a final mAP of 94. 1% . Since the obtained model is more accurate and smaller compared to YOLOv4,it can havea better performance when dealing with the deployment of? food analysis models in mobile and embedded devices to solve practical tasks.

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更新日期/Last Update: 2022-11-10