AI-based object detection, and efforts to explain and investigate their characteristics, is a topic of high interest. The impact of, e.g., complex background structures with similar appearances as the objects of interest, on the detection accuracy and, beforehand, the necessary dataset composition are topics of ongoing research. In this paper, we present a systematic investigation of background influences and different features of the object to be detected. The latter includes various materials and surfaces, partially transparent and with shiny reflections in the context of an Industry 4.0 learning factory. Different YOLOv8 models have been trained for each of the materials on different sized datasets, where the appearance was the only changing parameter. In the end, similar characteristics tend to show different behaviours and sometimes unexpected results. While some background components tend to be detected, others with the same features are not part of the detection. Additionally, some more precise conclusions can be drawn from the results. Therefore, we contribute a challenging dataset with detailed investigations on 92 trained YOLO models, addressing some issues on the detection accuracy and possible overfitting.

本研究解决了AI基础目标检测在复杂背景情况下的准确性问题，特别是在工业4.0学习工厂环境下。论文提出了一种系统性的方法，深入探讨了背景因素和目标特征的影响，并通过训练92个不同的YOLOv8模型，揭示了特定材料和表面特征对检测结果的复杂影响，进而提供了一个具有挑战性的训练数据集。

使用YOLOv8在学习工厂环境中进行目标检测特征研究