论文标题
使用Yolov4使用转移学习在印度食品盘中检测物体检测4
Object Detection in Indian Food Platters using Transfer Learning with YOLOv4
论文作者
论文摘要
对象检测是计算机视觉中的一个众所周知的问题。尽管如此，它在传统的印度美食菜肴中的使用和普遍性仍然有限。特别是，由于三个原因，认识到一张照片中存在的印度食品菜肴具有挑战性。我们通过提供全面标记的印度食品数据集-1010的标签来解决这些问题，该数据集包含10种经常出现在印度主食中的食品类别，并使用Yolov4对象探测器模型使用转移学习。我们的模型能够达到10级数据集的总体地图分数为91.8％，F1得分为0.90。我们还提供了10个级别数据集-20的延伸，其中包含10种传统的印度食品类别。
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.
