TY - GEN
T1 - LOCO
T2 - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
AU - Mayershofer, Christopher
AU - Holm, Dimitrij Marian
AU - Molter, Benjamin
AU - Fottner, Johannes
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Machine perception is a key challenge towards autonomous systems. Especially in the field of computer vision, numerous novel approaches have been introduced in recent years. This trend is based on the availability of public datasets. Logistics is one domain that could benefit from such innovations. Yet, there are no public datasets available. Accordingly, we create the first public dataset for scene understanding in logistics. The Logistics Objects in COntext (LOCO) dataset contains 39,101 images. In its first release there are 5,593 bounding-box annotated images. In total 151,428 instances of pallets, small load carriers, stillages, forklifts and pallet trucks were annotated. We also present and discuss our data acquisition approach which features enhanced privacy protection for workers. Finally, we provide an in-depth analysis of LOCO, compare it to other datasets (i.e. OpenImages and MS COCO) and show that it has far more annotations per image and also a considerably smaller annotation size. The dataset and future extensions will be available on our website (https://github.com/tum-fml/loco).
AB - Machine perception is a key challenge towards autonomous systems. Especially in the field of computer vision, numerous novel approaches have been introduced in recent years. This trend is based on the availability of public datasets. Logistics is one domain that could benefit from such innovations. Yet, there are no public datasets available. Accordingly, we create the first public dataset for scene understanding in logistics. The Logistics Objects in COntext (LOCO) dataset contains 39,101 images. In its first release there are 5,593 bounding-box annotated images. In total 151,428 instances of pallets, small load carriers, stillages, forklifts and pallet trucks were annotated. We also present and discuss our data acquisition approach which features enhanced privacy protection for workers. Finally, we provide an in-depth analysis of LOCO, compare it to other datasets (i.e. OpenImages and MS COCO) and show that it has far more annotations per image and also a considerably smaller annotation size. The dataset and future extensions will be available on our website (https://github.com/tum-fml/loco).
KW - Dataset
KW - Logistics
KW - Object Detection
KW - Perception
UR - http://www.scopus.com/inward/record.url?scp=85102197534&partnerID=8YFLogxK
U2 - 10.1109/ICMLA51294.2020.00102
DO - 10.1109/ICMLA51294.2020.00102
M3 - Conference contribution
AN - SCOPUS:85102197534
T3 - Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
SP - 612
EP - 617
BT - Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
A2 - Wani, M. Arif
A2 - Luo, Feng
A2 - Li, Xiaolin
A2 - Dou, Dejing
A2 - Bonchi, Francesco
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 December 2020 through 17 December 2020
ER -