Logistics code identification and sorting method based on multi-task deep learning

Abstract

The invention provided a logistics code identification and sorting method based on multi-task deep learning. A method used for acquiring logistics code label images in all directions, a designing scheme capable of conveniently and visually detecting and positioning logistics code labels, a designing scheme suitable for character positioning and the segmented character size, character pattern and intervals between characters of the logistics code labels, a Faster R-CNN network used for detecting and positioning the logistics code labels, an algorithm module used for deviation rectifying of thelogistics code labels and detecting regular and reverse states of the characters, a character segmentation algorithm module used for conducting segmentation treatment on the characters on the logistics code labels, a multitask deep convolutional neural network used for deep learning and training identification, and a sorting control module used for controlling the sorting action according to the identified logistics code are included. According to the logistics code identification and sorting method based on multi-task deep learning, the problem that a large number of goods with irregular shapes and flexible packaging cannot be quickly and automatically sorted is effectively solved.

Description

technical field [0001] The invention relates to the application of artificial intelligence, digital image processing, convolutional neural network and computer vision in the identification and sorting of logistics goods labels, and belongs to the field of intelligent logistics. Background technique [0002] The real-time collection of goods packaging appearance information in supply chain logistics operations is an important prerequisite for realizing logistics automation and intelligence. The most frequent operation is the out / in warehouse operation and the automatic identification and sorting of goods in the distribution center. The application of the online image recognition technology of goods in the out / in warehouse and automatic sorting process of the distribution center has good expansion value. [0003] The current mainstream method mainly completes the identification of logistics codes through two-dimensional identification codes and radio frequency identification m...

AI Technical Summary

Problems solved by technology

[0007] 1) High cost, susceptible to external environmental interference and affect the correct identification of information;

[0008] 2) Upstream suppliers are required to provide corresponding cooperation in the production process. There is currently no unified standard for RFID, and the coding standard system is complicated;

[0009] 3) Barcode and RFID technology alone cannot detect the integrity of the appearance of the goods, etc.

Logistics codes will be affected by factors such as temperature, humidity, and light, resulting in deformation, blurring, missing, and falling off, which will reduce the effectiveness of logistics code identification and affect the smooth progress of the entire logistics transportation business.

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