Recognition method research on rough handling of express parcels based on acceleration features and CNN. (15th October 2020) - Ding, Ao; Zhang, Yuan; Zhu, Lei; Du, Yanping; Ma, Luping
Highlights: Three typical rough handling of parcels in the express industry were summarized. The method to identify the kind of rough handling with 96% accuracy was proposed. A hardware device to collect three-axis acceleration of parcels was developed. A new feature expression was designed to suit the actual physical background. A CNN model commonly used in visual recognition was modified to use in the method. Abstract: Rough Handling of Express Parcels (RHEP) increases the risk of courier cargo damage and hurts the reputation of the express industry. Meanwhile, RHEP indirectly caused excessive use of packaging materials and cushioning materials, thereby aggravating environmental and social problems, such as waste disposal pressure and waste of resources. The prerequisite to prevent RHEP is recognition of it. Three typical types of RHEP (dropping, kicking fast and throwing) are discussed in this paper. For these types of RHEP, an intelligent recognition method is proposed. The main idea is to window the acceleration data of the package and extract the features in the window, then arrange these feature data into a three-dimensional matrix as the input of the CNN, and finally obtain the recognition result. In the study, mean, variance, kurtosis, skewness, dynamic range, short-term energy, and zero-crossing rate are considered to be good features in the issues dealt with in this paper. The feature data are organized in a way that different features occupy different channels. Within the uniform channel, three rows correspond to three axes, and the time window order corresponds to consecutive columns. This arrangement of feature data can take full advantage of convolution operations to mine the potential information of acceleration signals with time series characteristics and spatial characteristics. Experimental results show that the recognition accuracy of this method can reach 93.2–96.12% steadily, which is better than the performance of directly arranging features into vectors and sending it to the fully connected network (recognition accuracy is 85–95% and fluctuates greatly). Combining the recognition results of this method with the time and place of RHEP can more clearly analyze the causes of RHEP and propose targeted preventive measures.