DMZoomNet: Improving Object Detection Using Distance Information in Intralogistics Environments

In the field of the intralogistics industry, we present DMZoomNet, a novel architecture that combines deep learning-based detectors with distance information to enhance object detection performance. Evaluation of our approach is conducted using the LOCO dataset, one of the few open source datasets available specifically designed for intralogistics scenarios. By comparing DMZoomNet with existing detectors and object detection methods, we demonstrate its superiority in several object detection metrics within complex intralogistics environments, such as warehouses densely packed with objects. This work contributes to the advancement of object detection techniques in the intralogistics industry and paves the way for future research and applications in this domain.