Indonesian food identification and detection in the smart nutrition box using faster-RCNN

Sigit Adinugroho, P. P. Adikara, Edy Santoso, Restu Amara, Kresentia Septiana, Kenza Dwi Anggita
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引用次数: 2

Abstract

Food detection and localization are useful to recognize consumer preference and the amount of consumption. In the end, they can be integrated to food production to reduce oversupply and food waste. In this paper, the Faster R-CNN model is exploited to locate and classify food objects in a tray box as a first stage for food loss quantization. To perform the objective, the model was trained on images scraped from the Internet. Then, performance evaluation was conducted on both complex image and tray box image. On tray box images data, the model was able to achieve Average Precision of 0.455 and Average Recall of 0.628 for IoU 0.50:0.95.
印尼食品识别检测在智能营养盒中使用更快的rcnn
食品检测和定位有助于识别消费者的偏好和消费量。最后,它们可以与粮食生产相结合,以减少供应过剩和粮食浪费。本文利用Faster R-CNN模型对托盘箱中的食物物体进行定位和分类,作为食物损失量化的第一阶段。为了实现这一目标,该模型使用从互联网上抓取的图像进行训练。然后分别对复合图像和托盘盒图像进行性能评价。在托盘盒图像数据上,对于IoU 0.50:0.95,该模型的平均精密度为0.455,平均召回率为0.628。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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