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.

翻译：基于人工智能的物体检测及其特性解释与探究是当前备受关注的研究方向。复杂背景结构（例如与目标物体外观相似）对检测精度的影响，以及前期必要的数据集构建方法，均是持续探索的课题。本文针对背景干扰因素及待检测物体的不同特征进行了系统性研究。后者在工业4.0学习工厂的背景下，涵盖了多种材质与表面特性，包括部分透明材质及具有镜面反射的物体。我们针对每种材质使用不同规模的数据集分别训练了多个YOLOv8模型，其中仅外观参数发生变化。实验结果表明，相似特性往往呈现不同的检测行为，有时甚至产生预期之外的结果。部分背景元素容易被误检，而具有相同特征的其他背景却未被识别。此外，从实验结果中可以得出若干更精确的推论。因此，本研究通过92个已训练YOLO模型的详细分析，构建了一个具有挑战性的数据集，对检测精度与可能存在的过拟合问题进行了深入探讨。