Abstract
Traditional manual sorting has problems such as low efficiency, low automation level and increased costs, which are difficult to meet the sorting challenges brought by the rapid development of logistics industry. In addition, it is difficult for existing parcel box detection algorithms to strike a balance between identification efficiency, identification accuracy and deployment cost. In this paper, we propose a 3D localization algorithm for rectangular packaging boxes based on deep learning, and design a lightweight parcel box detection model, the Efficient Object detection Network (EODNet). Linear attention mechanism is used in the backbone of the model to achieve efficient feature selection with low-cost computing resources. The high-low layer feature fusion structure and C2f-GhostCondConv are designed on the neck of the model to achieve the selective fusion of input features at different levels with small parameter number and computational amount. The effectiveness of the model improvement strategy and the universality of the detection model were verified on the packing box data set and the public data set. Moreover, the proposed algorithm achieved high accuracy in the parcel box size prediction experiment with an average error of less than 3.7% and took less than 10 ms.