Visual Sorting Method Based on Multi-Modal Information Fusion

Abstract

Visual sorting of stacked parcels is a key issue in intelligent logistics sorting systems. In order to improve the sorting success rate of express parcels and effectively obtain the sorting order of express parcels, a visual sorting method based on multi-modal information fusion (VS-MF) is proposed in this paper. Firstly, an object detection network based on multi-modal information fusion (OD-MF) is proposed. The global gradient feature is extracted from depth information as a self-attention module. More spatial features are learned by the network, and the detection accuracy is improved significantly. Secondly, a multi-modal segmentation network based on Swin Transformer (MS-ST) is proposed to detect the optimal sorting positions and poses of parcels. More fine-grained information of the sorting parcels and the relationships between them are gained by adding Swin Transformer models. Frequency domain information and depth information are used as supervision signals to obtain the pickable areas and infer the occlusion degrees of parcels. A strategy for the optimal sorting order is also proposed to ensure the stability of the system. Finally, a sorting system with a 6-DOF robot is constructed to complete the sorting task of stacked parcels. The accuracy and stability the system are verified by sorting experiments.