Carton dataset synthesis method for domain shift based on foreground texture decoupling and replacement

TL;DR

This paper introduces a novel image synthesis method that replaces foreground textures to address domain shift in object detection, significantly improving model generalization across different scenarios in industrial datasets.

Contribution

A new foreground texture decoupling and replacement technique that enhances dataset diversity and model robustness across domains.

Findings

Boosts AP by 4.3%~6.5% on RetinaNet

Improves AP by 3.4%~6.8% on Faster R-CNN

Effectively reduces domain shift in industrial object detection

Abstract

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.