Research And Implementation Of Key Technologies For Spine Detection And Identification For Smart Bookcases

With the rapid development of artificial intelligence,in order to reduce the artificial cost,the intelligent management of books is an important research direction.This paper is oriented to the application of the intelligent bookcase,whose function is to complete the self-service borrowing and returning of books by detecting and identifying the spine of books.The existing spine detection and recognition methods are mainly based on RFID technology and image-based text recognition.However,RFID technology has high hardware cost,while image-based text recognition has low recognition accuracy.Different from the above two methods,especially the method based on image and character recognition,this paper transforms the spine detection and recognition into the spine detection based on case segmentation,spine image feature extraction and retrieval to solve the problem,and achieves good spine detection and recognition results in speed and accuracy.The details are as follows:(1)A spine detection method based on deep learning case segmentation is designed and implemented.On the basis of the example segmentation model of MASK R-CNN obtained from the pre training of COCO data set,the idea of migration learning is used to train the network again with the self built data set;For the spine with the same tilt angle,the Hough transform line detection is used for correction;For spine with different skew angles,a multi rotation based model of MASK R-CNN spine detection is proposed,which improves the accuracy and robustness of the algorithm;In order to improve the efficiency of spine detection,it improves the regional recommendation network,and uses the K-Means algorithm results to design a suitable spine anchor ratio.Experimental results show that this method is better than the previous spine detection method.(2)This paper studies and implements a SIFT feature point extraction method based on CUDA framework,which solves the problem of low effectiveness of SIFT under CPU.The parallelism of SIFT algorithm is analyzed,and the execution flow of sift in CUDA framework is designed.The differences between the implementation of SIFT algorithm in GPU and CPU are analyzed and compared.Experiments show that compared with the CPU's SIFT algorithm,the speed of feature extraction is improved by more than 10 times.(3)In this paper,a method based on SIFT image feature coding and retrieval spine recognition is proposed.This paper studies and analyzes the differences among three coding algorithms: VLAD(vector of locally aggregated descriptors),FV(Fisher vector)and BOF(bag of features).Aiming at the problem of high dimension of feature vector after coding,principal component analysis is used to reduce the dimension of feature to reduce the redundancy of feature;In order to improve the retrieval efficiency,a local sensitive hash retrieval algorithm is used,and the optimal parameters of the algorithm are selected through experiments;experiments are conducted to compare the impact of different feature coding,retrieval algorithm and feature representation on spine recognition rate,which proves that Compared with other spine recognition methods based on image feature retrieval,the performance and efficiency of this method are significantly improved.(4)This paper designs and implements an unmanned book management system for intelligent bookcase.The overall architecture and data model of the system are analyzed and designed;based on the C/S architecture,the app and windows spine detection and recognition algorithm verification program for mobile users is implemented,which provides users with the experience of book borrowing and returning without perception,and facilitates the administrator's management of the system information.