YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines

Data abstract: The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) Paper abstract: The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%.

数据集摘要：YogDATA数据集收录了工业实验室生产线在开展优质酸奶质检时采集的图像。针对酸奶杯识别的案例研究，需使用配套图像集对Mask R-CNN与YOLO v5.0模型进行训练。因此，采集配套图像以训练并评估该分类任务至关重要。具体而言，YogDATA数据集为Mask R-CNN与YOLO系列模型提供了统一的标注数据：针对YOLO架构，训练与验证数据集包含JPG格式图像集及其TXT格式标注文件；针对Mask R-CNN架构，同批次图像的标注以JSON格式存储，且每个子集的图像与标注按80%划入训练集、20%划入测试集的比例划分。

论文摘要：传统工业流程的数字化浪潮正持续为现代社会带来积极影响，为经济发展提供了关键支撑。具体而言，乳制品行业涵盖诸多严苛且细致的生产流程，借助现代自动化工具与全流程工程解决方案提升生产效率、持续满足高标准行业要求至关重要。为此，本研究提出了一种基于机器视觉与人工智能的智能算法，可识别生产线中的乳制品。此外，为训练与验证该模型，本研究构建了包含生产线内酸奶杯图像的YogDATA数据集。具体而言，本研究评估了两种深度学习模型（Mask R-CNN与YOLO v5.0）对生产线中各酸奶杯的识别与检测能力，以实现产品包装流程的自动化。实验结果显示，两种模型的检测精度相近，均达到99%。