The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregula...The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregular and multi-scale nature of food images.Addressing these complexities,our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion,grounded in the ConvNeXt architecture.Our model employs hybrid attention(HA)mechanisms to pinpoint critical discriminative regions within images,substantially mitigating the influence of background noise.Furthermore,it introduces a multi-stage local fusion(MSLF)module,fostering long-distance dependencies between feature maps at varying stages.This approach facilitates the assimilation of complementary features across scales,significantly bolstering the model’s capacity for feature extraction.Furthermore,we constructed a dataset named Roushi60,which consists of 60 different categories of common meat dishes.Empirical evaluation of the ETH Food-101,ChineseFoodNet,and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%,82.86%,and 92.50%,respectively.These figures not only mark an improvement of 1.04%,3.42%,and 1.36%over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods.Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition,setting a new benchmark for the field.展开更多
基金广东省领军人才专项资金资助项目Irish Research Council for ScienceEngineering and Technology Under the Government of Ireland Postdoctoral Fellowship Scheme(PD/2011/2372)
文摘食品现代化生产、加工过程中需要对食品品质信息进行快速无损获取,以保证食品品质安全,满足消费者的需要.近年来国内频发的食品质量与安全问题也要求实现食品品质安全信息的客观准确检测.现代光学成像技术通过获取食品在不同光谱波段下的图像信息,然后采用数字图像处理算法进行特征信息提取,并通过模式识别算法建立食品品质定量关系模型,从而实现食品品质信息的快速、无损、高效、低成本检测,为食品现代化加工流程中的自动控制和分级管理与监控提供信息支持.孙大文院士领导的爱尔兰国立都柏林大学(University College Dublin,UCD)食品冷冻与计算机化食品技术(Food Refrige-ration&Computerised Food Technology,FRCFT)研究所在应用包括计算机视觉技术和高光谱成像技术在内的多种现代光学成像技术对食品品质快速无损检测这一领域的研究工作一直处于世界领先水平,研究成果得到了国际同行的广泛认可和高度关注.本文综述了孙大文院士及其团队过去十余年间在相关领域取得的一系列原创性研究成果.
基金The support of this research was by Hubei Provincial Natural Science Foundation(2022CFB449)Science Research Foundation of Education Department of Hubei Province(B2020061),are gratefully acknowledged.
文摘The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregular and multi-scale nature of food images.Addressing these complexities,our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion,grounded in the ConvNeXt architecture.Our model employs hybrid attention(HA)mechanisms to pinpoint critical discriminative regions within images,substantially mitigating the influence of background noise.Furthermore,it introduces a multi-stage local fusion(MSLF)module,fostering long-distance dependencies between feature maps at varying stages.This approach facilitates the assimilation of complementary features across scales,significantly bolstering the model’s capacity for feature extraction.Furthermore,we constructed a dataset named Roushi60,which consists of 60 different categories of common meat dishes.Empirical evaluation of the ETH Food-101,ChineseFoodNet,and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%,82.86%,and 92.50%,respectively.These figures not only mark an improvement of 1.04%,3.42%,and 1.36%over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods.Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition,setting a new benchmark for the field.
