  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 warehourses, and objects are labeled using per-instance
segmentation for precise localization. There are totally 250,000 instance masks
from 16,136 images. In addition, we design a carton detector based on RetinaNet
by embedding 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 while BGS guides the detector to pay more attention to
boundary information of cartons and decouple repeated carton textures. To
demonstrate the generalization of OPCL to other datasets, we conduct extensive
experiments on MS COCO and PASCAL VOC. The improvement of AP on MS COCO and
PASCAL VOC is 1.8% - 2.2% and 3.4% - 4.3% respectively.
