DOI: 10.5281/zenodo.14044727
title: Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset

description: This dataset was published with the dataset descriptor "Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset".

ACKNOWLEDGEMENTS

The project ”ZUKIPRO” is funded as part of the ”Future Centers” program by the FederalMinistry of Labour and Social Affairs and the European Union through the European SocialFund Plus (ESF Plus).Roles and Contributions.
description: Machine learning (ML), particularly within the domain of computer vision (CV), has establishedsolutions for automated quality classification using visual data in manufacturing processes.Object detection as a CV method for quality classification provides a distinct advantagein enabling the assessment of items within the manufacturing environment, regardless oftheir location in images. However, substantial challenges remain regarding labeled dataavailability in manufacturing contexts, training examples, data imbalance, and the complexityof incorporating these methods into real-world applications. Furthermore, real-world datasetsoften lack adherence to FAIR principles, which limits their accessibility and interoperability,especially for small- and medium-sized enterprises (SMEs) working to integrate objectdetection into their manufacturing processes. In this article, we present a low-resolution640x640 dataset based on plastic bricks for object detection, featuring two quality labels toidentify minor surface defects as an example of quality classification. We analyze the datasetusing a YOLOv5 model on three different dataset sizes, while accounting for class imbalance,to demonstrate the accuracy of an object detection model in a simple manufacturing usecase. The mean Average Precision mAP@0.5 for correctly identifying instances in ourtesting dataset ranges from 0.668 to 0.774, depending on dataset size and class imbalance.While our focus is on demonstrating object detection with low-resolution images and limiteddata availability, the generated data and trained model also adhere to FAIR principles.Therefore, these resources are made available with proper metadata to support their reuseand further investigation into object detection tasks for similar quality classification use casesin manufacturing.
description: This dataset was exported via roboflow.com on September 14, 2023 at 11:14 AM GMT

Roboflow is an end-to-end computer vision platform that helps you* collaborate with your team on computer vision projects* collect & organize images* understand and search unstructured image data* annotate, and create datasets* export, train, and deploy computer vision models* use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset,visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 1500 images of Lego plastic breaks. The total dataset compromises a size of 140.01 MB.Lego-bricks are annotated in YOLO v5 PyTorch format. The measured variable (label) are defects on the bricks.

The following pre-processing was applied to each image:* Auto-orientation of pixel data (with EXIF-orientation stripping)* Resize to 640x640 (Stretch)

No image augmentation techniques were applied.