Abstract
The rough handling of express parcels increases the risk of damage to goods, brings customer complaints, and causes over-packing problems. The prerequisite for solving the rough handling of express parcels is to identify various typical rough handling intelligently. Therefore, an intelligent recognition method based on the CNN-GRU (Convolutional Neural Networks-Gated Recurrent Units) fusion model with the channel attention mechanism is proposed in this paper. First, the collected triaxial acceleration data of the parcel are intercepted and windowed. Then seven traditional features (mean, variance, kurtosis, skewness, dynamic range, short-term energy, and zero-crossing rate) are extracted in the window. The traditional feature data is arranged in a matrix of 3 axes × 50 time windows × 7 features and normalized. Finally, the three-dimensional traditional feature matrix is input into the model to obtain the recognition results (normal, dropping, throwing, or kicking). A novel channel attention mechanism called CDCE (Channel Dense-Concatenation-Excitation) block is introduced into the CNN-GRU fusion model. Based on the Squeeze-Excitation Net, the CDCE block replaces the global pooling operation with the dense connection operation of sub-channels, and appropriately adjusts the subsequent layers, to achieve more precise parameter learning. Besides, a new data set has been collected and shared. Experiments show that the recognition accuracy of the CNN-GRU model with the CDCE blocks can reach 96.04%, which is about 1.37% higher than that of the CNN model in the previous study. Moreover, the size of the CNN-GRU model with the CDCE blocks is reduced to 7% of the size of the CNN model.
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Acknowledgements
Thanks to the logistics engineering team of the School of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, for supporting our research work. Thanks to the volunteers who participated in the data collection. And thank the funders for their funding.
Funding
This work was partially funded by the Key Technologies Research and Development Program (Grand number 2018YFB1403103), Key Project of Basic Research of Beijing Institute of Graphic Communication (Grand number Ea202001) and Research Project of Beijing Institute of Graphic Communication (Grand number Ec201807).
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AD: Investigation, methodology, software, validation, visualization, writing—original draft. YZ: Investigation, data curation, project administration, supervision, conceptualization, writing—review and editing. LZ: Resources, funding acquisition, conceptualization, formal analysis, project administration, writing—review and editing. HL: Funding acquisition, project administration, supervision. LH: Investigation, visualization.
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Ding, A., Zhang, Y., Zhu, L. et al. Intelligent recognition of rough handling of express parcels based on CNN-GRU with the channel attention mechanism. J Ambient Intell Human Comput 14, 973–990 (2023). https://doi.org/10.1007/s12652-021-03350-2
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DOI: https://doi.org/10.1007/s12652-021-03350-2