Refereed article in conference proceedings (A4)

Towards Autonomous Industrial Warehouse Inspection




List of Authors: Farahnakian Fahimeh, Koivunen Lauri, Mäkilä Tuomas, Heikkonen Jukka

Conference name: International Conference on Automation and Computing

Publication year: 2021

Book title *: 2021 26th International Conference on Automation and Computing (ICAC)

ISBN: 978-1-6654-4352-4

eISBN: 978-1-86043-557-7

DOI: http://dx.doi.org/10.23919/ICAC50006.2021.9594180


Abstract

In order to achieve autonomous warehouse inspection, a reliable rack monitoring and instant detection of rack is necessary. Damage detection is an essential task for pallet rack maintenance and it requires large amount of manual efforts. To address this problem, we employ deep learning to automatically detect damages with their per-pixel segmentation mask in this paper. We also investigated the impact of using related and unrelated data on the detection performance. For this purpose, we compared the performance of a detector when it is trained on: (1) the COCO dataset, (2) the ImageNet dataset and (3) a related task such as car damage dataset. Moreover, we evaluated the performance of the proposed detector based on different backbones. Experiments show that the detector with ResNet101 as feature extractor can achieve 93.45% accuracy in our real dataset. The code and dataset can be viewed at: https://gitlab.utu.fi/drone-warehouse/beam_defect_detection.git.


Last updated on 2021-16-11 at 12:00