We consider the problem of automatic pallet detection in industrial environments using a single RGB camera. The problem is relevant in the context of autonomous guided vehicle navigation, and for tasks like pallet storage and retrieval. In particular, we present an approach based on a convolutional neural network (CNN) followed by a decision making step. The convolutional neural network is trained to recognize two elements of a pallet, namely the pallet front side and its pockets. We also report a comparison between three state-of-the-art CNNs: Faster R-CNN, SSD and YOLOv4. For training and evaluation, a dataset was collected in a warehouse. The dataset contains images of pallets in different configurations, either on the ground or on racks, and with arbitrary orientation. Overall, results indicate that Faster R-CNN and SSD perform better than YOLOv4.

A Comparison of Deep Learning Models for Pallet Detection in Industrial Warehouses / Zaccaria, M.; Monica, R.; Aleotti, J.. - (2020), pp. 417-422. ((Intervento presentato al convegno 16th IEEE International Conference on Intelligent Computer Communication and Processing, ICCP 2020 tenutosi a rou nel 2020 [10.1109/ICCP51029.2020.9266168].

A Comparison of Deep Learning Models for Pallet Detection in Industrial Warehouses

Zaccaria M.;Monica R.;Aleotti J.
2020

Abstract

We consider the problem of automatic pallet detection in industrial environments using a single RGB camera. The problem is relevant in the context of autonomous guided vehicle navigation, and for tasks like pallet storage and retrieval. In particular, we present an approach based on a convolutional neural network (CNN) followed by a decision making step. The convolutional neural network is trained to recognize two elements of a pallet, namely the pallet front side and its pockets. We also report a comparison between three state-of-the-art CNNs: Faster R-CNN, SSD and YOLOv4. For training and evaluation, a dataset was collected in a warehouse. The dataset contains images of pallets in different configurations, either on the ground or on racks, and with arbitrary orientation. Overall, results indicate that Faster R-CNN and SSD perform better than YOLOv4.
978-1-7281-9080-8
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2886060
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