文章基本信息

标题：Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment
本地全文：下载
作者：Ke Yang ; Baoliang Peng ; Fengwei Gu 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2022
卷号：11
期号：15
DOI：10.3390/foods11152197
语种：English
出版社：MDPI Publishing
摘要：Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning—a convolutional neural network—is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.
关键词：convolutional neural network;YOLO;object detection;garlic root cutting;food safety control