LOCO: Logistics Objects in Context

Abstract

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).

Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities - A Framework and Dataset Creation Approach

“…The creation of industry related datasets is the topic of this subsection. Obtaining and marking such datasets in an industrial environment can be difficult due to factors such as it being time-consuming, susceptible to human mistakes, and constrained by various privacy and security regulations [34,43,44]. Therefore, using a semi-or fully-automated pipeline for the dataset creation should be considered.…”

“…FRCNN ResNet50 surpasses SSD DL model in terms of detecting stillages, transport robots, dollies, and pallets with the Average Precision (AP) metric at 0.5 are 69.90%, 89.93%, and 48.60%, respectively. The recordings of the LOCO [34] dataset are captured using different types of cameras with diverse fields of view and resolutions in a real warehousing environment. The cameras are set up on a mobile unit with a special arm, thus enabling the re-adjustment of the camera's point of view.…”

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

“…Mayershofer et al [May+20] present a dataset consisting of 39,101 images in logistics environments, of which 5,593 images are annotated. Annotations are performed manually for five logistics specific object categories: small load carrier, pallet, stillage, forklift and pallet truck.…”

“…Summary: Item recognition has been investigated for parcels [Nau+20; FZL21; Nau+22], small load carriers [MGF20; Dör+21] and general logistics objects such as pallets and forklifts [May+20]. Note, that other approaches also rely on object detection as part of their pipeline, however, this section focused on research where object detection is the main point of interest.…”

“…The availability of freely available datasets is limited, as we can infer from Tables 1, 2, 3 and 4. Datasets exist for instance segmentation of logistics objects [May+20] and parcels [Nau+20]. Furthermore, chipwood re-identification [Rut+21; RU22] datasets have been presented.…”