  One major impediment in rapidly deploying object detection models for
industrial applications is the lack of large annotated datasets. We currently
have presented the Sacked Carton Dataset(SCD) that contains carton images from
three scenarios, such as comprehensive pharmaceutical logistics company(CPLC),
e-commerce logistics company(ECLC), fruit market(FM). However, due to domain
shift, the model trained with one of the three scenarios in SCD has poor
generalization ability when applied to the rest scenarios. To solve this
problem, a novel image synthesis method is proposed to replace the foreground
texture of the source datasets with the texture of the target datasets. Our
method can keep the context relationship of foreground objects and backgrounds
unchanged and greatly augment the target datasets. We firstly propose a surface
segmentation algorithm to achieve texture decoupling of each instance.
Secondly, a contour reconstruction algorithm is proposed to keep the occlusion
and truncation relationship of the instance unchanged. Finally, the Gaussian
fusion algorithm is used to replace the foreground texture from the source
datasets with the texture from the target datasets. The novel image synthesis
method can largely boost AP by at least 4.3%~6.5% on RetinaNet and 3.4%~6.8% on
Faster R-CNN for the target domain. Code is available at
https://github.com/hustgetlijun/RCAN.
