# SCD: A Stacked Carton Dataset for Detection and Segmentation Source: [SCD: A Stacked Carton Dataset for Detection and Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:58:51.104Z Authors: Jinrong Yang, Shengkai Wu, Lijun Gou, Hangcheng Yu, Chenxi Lin, Jiazhuo Wang, Minxuan Li, Xiaoping Li Published: 2021-02-25T12:22:12.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()