Detection of mold on the food surface using YOLOv5.
Detection of mold on the food surface using YOLOv5.
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DOI：
10.1016/j.crfs.2021.10.003
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发表时间：
2021
影响因子：
6.3
通讯作者：
Islam A
中科院分区：
文献类型：
--
作者：
Jubayer F;Soeb JA;Mojumder AN;Paul MK;Barua P;Kayshar S;Akter SS;Rahman M;Islam A
该研究旨在识别在各种食品表面生长的不同霉菌。因此，我们基于“只看一次（YOLO）v5”原则进行了食品表面霉菌检测的案例研究。在此背景下，创建了包含 2050 张表面长有霉菌的食物图像的数据集。图像来自我们自己的实验室（850 张图像）以及互联网（1200 张图像）。该数据集使用预训练的 YOLOv5 算法进行训练。还进行了实验室测试，以确认生长的生物体是霉菌。与YOLOv3和YOLOv4相比，当前的YOLOv5模型具有更好的精度、召回率和平均精度（AP），分别为98.10%、100%和99.60%。本研究首次使用YOLOv5算法来检测食品表面的霉菌。总之，所提出的模型成功地识别了食品表面上存在的任何类型的霉菌。我们目前正在使用 YOLOv5 进行研究，以确定检测到的霉菌的具体种类。有史以来第一个使用 YOLOv5 检测食品表面霉菌的方法。为此使用了包含 2050 张图像的数据集。 YOLOv5模型在检测食品表面霉菌方面具有更高更好的性能。
The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the “you only look once (YOLO) v5” principle. In this context, a dataset of 2050 food images with mold growing on their surfaces was created. Images were obtained from our own laboratory (850 images) as well as from the internet (1200 images). The dataset was trained using the pre-trained YOLOv5 algorithm. A laboratory test was also performed to confirm that the grown organisms were mold. In comparison to YOLOv3 and YOLOv4, this current YOLOv5 model had better precision, recall, and average precision (AP), which were 98.10%, 100%, and 99.60%, respectively. The YOLOv5 algorithm was used for the first time in this study to detect mold on food surfaces. In conclusion, the proposed model successfully recognizes any kind of mold present on the food surface. Using YOLOv5, we are currently conducting research to identify the specific species of the detected mold. First approach ever to detect the mold on food surfaces using YOLOv5. A dataset of 2050 images was used in this purpose. The YOLOv5 model has a higher and better performance in detecting mold on food surfaces.
影响因子：
2.3
作者：
Kasper-Eulaers, Margrit;Hahn, Nico;Kummervold, Per Egil
通讯作者：
Kummervold, Per Egil
影响因子：
5
作者：
Yan, Bin;Fan, Pan;Yang, Fuzeng
通讯作者：
Yang, Fuzeng
影响因子：
3.6
作者：
Pan, Chen;Yan, Wei Qi
通讯作者：
Yan, Wei Qi