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SCD: A Stacked Carton Dataset for Detection and Segmentation

State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Faculty of Arts and Science, Queen’s University, Kingston, ON K7L 3N6, Canada
Author to whom correspondence should be addressed.
Academic Editor: Anastasios Doulamis
Sensors 2022, 22(10), 3617;
Received: 4 March 2022 / Revised: 29 April 2022 / Accepted: 6 May 2022 / Published: 10 May 2022
(This article belongs to the Special Issue Image Processing and Analysis for Object Detection)
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 and 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 article, 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 were collected from the Internet and several warehouses, and objects were labeled for precise localization using instance mask annotation. There were a total of 250,000 instance masks from 16,136 images. Naturally, a suite of benchmarks was established with several popular detectors and instance segmentation models. In addition, we designed a carton detector based on RetinaNet by embedding our proposed Offset Prediction between the Classification and Localization module (OPCL) and the Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality, which boosts AP by 3.14.7% on SCD at the model level, while BGS guides the detector to pay more attention to the boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL for other datasets, we conducted extensive experiments on MS COCO and PASCAL VOC. The improvements in AP on MS COCO and PASCAL VOC were 1.82.2% and 3.44.3%, respectively. View Full-Text
Keywords: object detection; larger-scale dataset; stacked carton object detection; larger-scale dataset; stacked carton
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MDPI and ACS Style

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.

AMA Style

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(10):3617.

Chicago/Turabian Style

Yang, Jinrong, Shengkai Wu, Lijun Gou, Hangcheng Yu, Chenxi Lin, Jiazhuo Wang, Pan Wang, Minxuan Li, and Xiaoping Li. 2022. "SCD: A Stacked Carton Dataset for Detection and Segmentation" Sensors 22, no. 10: 3617.

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