Dai Ning, Chen Jingchao, Hu Xudong, Yuan Yanhong
Key Laboratory of Modern Textile Machinery and Technology of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
Sci Rep. 2025 Jul 1;15(1):21980. doi: 10.1038/s41598-025-07730-y.
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.
传统的人工分拣存在效率低、自动化程度低和成本增加等问题，难以应对物流业快速发展带来的分拣挑战。此外，现有的包裹箱检测算法难以在识别效率、识别准确率和部署成本之间取得平衡。在本文中，我们提出了一种基于深度学习的矩形包装盒三维定位算法，并设计了一种轻量级包裹箱检测模型——高效目标检测网络（EODNet）。该模型的主干中使用了线性注意力机制，以利用低成本计算资源实现高效的特征选择。在模型的颈部设计了高低层特征融合结构和C2f-GhostCondConv，以小参数数量和计算量实现不同层次输入特征的选择性融合。在包装盒数据集和公共数据集上验证了模型改进策略的有效性和检测模型的通用性。此外，所提出的算法在包裹箱尺寸预测实验中达到了高精度，平均误差小于3.7%，且耗时不到10毫秒。
