Jonas Maximilian Werheid  , shengjie he, Tobias Hamann, Anas Abdelrazeq, Robert Schmitt

Machine learning (ML), particularly within the domain of computer vision (CV), has established solutions for automated quality classification using visual data in manufacturing processes. Object detection as a CV method for quality classification provides a distinct advantage in enabling the assessment of items within the manufacturing environment regardless of their location in images. However, there are substantial challenges regarding labeled data availability in manufacturing contexts, training examples, and the complexity of incorporating within the subject. Real-world datasets present challenges in high resolutions and task specificity that hinder the adoption of object detection by small- and middle-sized enterprises (SMEs) for their manufacturing processes. In this article, we present a simple 640x640 low-resolution dataset based on plastic bricks for object detection, featuring two quality labels to identify minor surface defects in some instances as an example of quality classification. Analyzing our dataset with a YOLOv5 model on four different dataset sizes, we aim to demonstrate the accuracy of a common object detection model in a simple manufacturing use case, showcasing object detection with low-resolution images and the impact of varying data availability. The mean Average Precision mAP@0.5:0.95 in correctly identifying instances improved from 0.786 to 0.833 as we moved from the smallest data size of 485 instances to the complete dataset of about 1500 instances. While our interest is specifically in showcasing object detection for manufacturing with low-resolution images and limited data availability, the generated data and trained model can serve as a common basis to further investigate object detection tasks on a wider variety of similar quality classification use cases in manufacturing.