Airport storehouse luggage retrieval method based on improved convolutional network

Abstract

The invention provides an airport storehouse luggage retrieval method based on an improved convolutional network. The method is characterized in that firstly, a network structure is improved, a new Asymbiotic and Mopping Convolution Block is provided, the structure extracts features through a plurality of branches to enhance the expression ability of a network convolution layer, and then the classification accuracy and robustness of the network to images are improved. The network training is carried out by using metric learning loss, so that the discrimination of luggage features is enhanced. Experimental results show that the provided luggage classification model has a good classification effect. In the image retrieval step, in order to enrich information contained in features, a multi-layer feature fusion method based on spatial pyramid pooling features and full connection layer features is provided, and a deep hash model capable of being used for luggage image retrieval is established in combination with a mixed loss function containing classification loss and quantization loss. Experiments show that the provided model has a better retrieval effect, the highest accuracy rate can reach 96.1%, and meanwhile time and space expenses are greatly reduced.

Description

technical field [0001] The invention relates to the technical field of airport warehouse luggage management, in particular to an airport warehouse luggage retrieval method based on an improved convolutional network. Background technique [0002] In recent years, with the increase in the number of civil aviation passengers and the amount of luggage checked in year by year, there are more and more cases of abnormal luggage such as loss and damage. However, at present, the civil aviation baggage center still manages abnormal baggage manually. According to the requirements of airlines, each piece of baggage has a lot of description fields. Cancel the luggage on the remarks column of the overcharged book. This method has a huge workload and is prone to errors. It is also difficult to modify, manage and query luggage information in the later stage. [0003] Before the development of deep learning technology matures, the traditional feature extraction method generally uses the co...

AI Technical Summary

Problems solved by technology

[0005] Although directly using network features for retrieval has high accuracy, when the dimensionality of the features is high, the retrieval speed is often slow and the space occupied by the features is also large.

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