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SCD: A Stacked Carton Dataset for Detection and Segmentation.

Jinrong Yang1, Shengkai Wu1, Lijun Gou1

  • 1State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary

Researchers developed the Stacked Carton Dataset (SCD) for improved carton detection in logistics. Novel methods, Offset Prediction between Classification and Localization (OPCL) and Boundary Guided Supervision (BGS), enhance detection accuracy.

Keywords:
larger-scale datasetobject detectionstacked carton

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Carton detection is crucial for automated logistics systems, including stacking, unstacking, and container unloading.
  • The lack of a large-scale, public carton dataset has hindered research and development in carton detection models.

Purpose of the Study:

  • To introduce the Stacked Carton Dataset (SCD), a large-scale dataset designed to advance carton detection research.
  • To establish benchmarks and evaluate existing models on the new dataset.
  • To propose novel techniques for improving carton detection performance.

Main Methods:

  • Collected and annotated 16,136 images with 250,000 instance masks to create the Stacked Carton Dataset (SCD).
  • Established benchmarks using popular object detectors and instance segmentation models on the SCD.
  • Designed a novel carton detector integrating Offset Prediction between Classification and Localization (OPCL) and Boundary Guided Supervision (BGS) with RetinaNet.

Main Results:

  • The proposed OPCL module improved Average Precision (AP) by 3.1–4.7% on the SCD at the model level.
  • The BGS module enhanced carton boundary detection and addressed issues with repeated textures.
  • OPCL demonstrated generalization, achieving 1.8–2.2% AP improvement on MS COCO and 3.4–4.3% on PASCAL VOC.

Conclusions:

  • The Stacked Carton Dataset (SCD) provides a valuable resource for advancing carton detection research.
  • The proposed OPCL and BGS modules significantly improve carton detection accuracy and generalization.
  • This work paves the way for more robust and efficient automated logistics systems.