论文标题
通过机器学习对机车数据集的批判性评估
Critical Evaluation of LOCO dataset with Machine Learning
论文作者
论文摘要
目的：对象检测正在通过自动化系统中的机器学习技术迅速发展。准备好的数据对于训练算法是必要的。因此，本文的目的是描述上下文（Loco）数据集中所谓的物流对象的重新评估，该数据集是内在物质学领域中的第一个用于对象检测的数据集。 方法论：我们使用三个步骤的实验研究方法来评估Loco数据集。首先，分析了GitHub上的图像以更好地了解数据集。其次，Google Drive Cloud用于培训目的，以重新访问算法实现和培训。最后，如果可以与原始出版物相比，请检查机车数据集，如果可以实现相同的培训结果。 研究结果：在我们的研究中实现的平均平均精度是对象检测中的常见基准，比最初的Loco作者的初步研究显着增加，获得了41％的幅度。但是，在叉车和托盘卡车的物体类型中特别看到了改进潜力。 独创性：本文介绍了Loco数据集的首次关键复制研究，以用于内部学术中的对象检测。它表明，基于机车的更好的超参数培训甚至比原始出版物中提出的更高的精度。但是，还有进一步改善机车数据集的空间。
Purpose: Object detection is rapidly evolving through machine learning technology in automation systems. Well prepared data is necessary to train the algorithms. Accordingly, the objective of this paper is to describe a re-evaluation of the so-called Logistics Objects in Context (LOCO) dataset, which is the first dataset for object detection in the field of intralogistics. Methodology: We use an experimental research approach with three steps to evaluate the LOCO dataset. Firstly, the images on GitHub were analyzed to understand the dataset better. Secondly, Google Drive Cloud was used for training purposes to revisit the algorithmic implementation and training. Lastly, the LOCO dataset was examined, if it is possible to achieve the same training results in comparison to the original publications. Findings: The mean average precision, a common benchmark in object detection, achieved in our study was 64.54%, and shows a significant increase from the initial study of the LOCO authors, achieving 41%. However, improvement potential is seen specifically within object types of forklifts and pallet truck. Originality: This paper presents the first critical replication study of the LOCO dataset for object detection in intralogistics. It shows that the training with better hyperparameters based on LOCO can even achieve a higher accuracy than presented in the original publication. However, there is also further room for improving the LOCO dataset.
