TITLE: Critical Evaluation of LOCO dataset with Machine Learning 

ABSTRACT: 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.
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.
Critical Evaluation of LOCO dataset with Machine Learning
http://arxiv.org/abs/2209.13499v1