研究工业场景下物体检测在复杂背景中的表现，提出92个模型的实证数据集。
Object detection characteristics in a learning factory environment using YOLOv8
- 用YOLOv8在不同材质和背景上训练，控制外观变量分析检测行为
- 部分相似背景会被误检，相同特征的却未被检测，结果反常
- 适合工业视觉、目标检测可靠性研究者参考
基于人工智能的目标检测及其特性解释是当前热点。复杂背景中与目标物外观相似的结构对检测精度的影响，以及前期所需数据集构成，仍是研究重点。本文系统研究了背景干扰及待检物体的多种特征，涵盖工业4.0学习工厂中的不同材质、表面属性，包括部分透明与高反射表面。针对每种材料，在不同规模的数据集上训练了多个YOLOv8模型，仅改变外观参数。结果发现，外观相似的物体表现出不一致的行为，有时出现意料之外的结果：某些背景成分被错误检测，而具有相同特征的其他背景则未被识别。基于此，我们构建了一个挑战性数据集，包含92个已训练的YOLO模型，深入揭示了检测准确率问题与潜在过拟合现象。
原文摘要 · Abstract (English)
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.
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