Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

SCD: Stacked Carton Scene Detection

Version 1 : Received: 4 March 2022 / Approved: 11 March 2022 / Online: 11 March 2022 (15:48:23 CET)

A peer-reviewed article of this Preprint also exists.

Yang, J.; Wu, S.; Gou, L.; Yu, H.; Lin, C.; Wang, J.; Wang, P.; Li, M.; Li, X. SCD: A Stacked Carton Dataset for Detection and Segmentation. Sensors 2022, 22, 3617. Yang, J.; Wu, S.; Gou, L.; Yu, H.; Lin, C.; Wang, J.; Wang, P.; Li, M.; Li, X. SCD: A Stacked Carton Dataset for Detection and Segmentation. Sensors 2022, 22, 3617.

Journal reference: Sensors 2022, 22, 3617
DOI: 10.3390/s22103617

Abstract

Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons, the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this paper, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images are collected from the Internet and several warehouses, and objects are labeled using per-instance segmentation for precise localization. There are total of 250,000 instance masks from 16,136 images. Naturelly, a suite of benchmarks are established with several popular detectors. In addition, we design a carton detector based on RetinaNet by embedding our proposed Offset Prediction between Classification and Localization module (OPCL) and Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality which boosts AP by 3.1%∼4.7% on SCD at the model level while BGS guides the detector to pay more attention to boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL to other datasets, we conduct extensive experiments on MS COCO and PASCAL VOC. The improvements of AP on MS COCO and PASCAL VOC are 1.8%∼2.2% and 3.4%∼4.3% respectively. Source dataset is available here.

Keywords

object detection; larger-scale dataset; stacked carton

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.