使用迁移学习和YOLOv4在印度食物拼盘中进行目标检测
Object Detection in Indian Food Platters using Transfer Learning with YOLOv4
摘要：
目标检测是计算机视觉中的一个众所周知的问题。尽管如此，它在传统印度菜肴中的使用和普及受到限制。特别是，识别一张照片中的印度菜肴是具有挑战性的，原因有三个：1。缺乏注释的印度食物数据集2。3.盘子之间的界限不明显。类内差异大。我们通过提供一个全面标记的印度食物数据集-IndianFood 10来解决这些问题，该数据集包含10种经常出现在印度主食中的食物类别，并使用带有YOLOv 4对象检测器模型的迁移学习。对于我们的10类数据集，我们的模型能够实现91.8%的总体mAP得分和0.90的f1得分。我们还提供了10类数据集的扩展-IndianFood 20，其中包含10个传统的印度食品类。
英文摘要：
Object detection is a well-known problem in computer vision. Despite this, its usage and pervasiveness in the traditional Indian food dishes has been limited. Particularly, recognizing Indian food dishes present in a single photo is challenging due to three reasons: 1. Lack of annotated Indian food datasets 2. Non-distinct boundaries between the dishes 3. High intra-class variation. We solve these issues by providing a comprehensively labelled Indian food dataset- IndianFood10, which contains 10 food classes that appear frequently in a staple Indian meal and using transfer learning with YOLOv4 object detector model. Our model is able to achieve an overall mAP score of 91.8% and f1-score of 0.90 for our 10 class dataset. We also provide an extension of our 10 class dataset- IndianFood20, which contains 10 more traditional Indian food classes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
