Research on Quick Inventory Framework Based on Deep Neural Network

Abstract

In view of the disadvantages of the existing popular RFID (Radio Frequency Identification) book inventory in the industry, such as expensive equipment, low accuracy, and high business threshold, this paper proposes a deep neural network-based inventory framework. By loading different deep neural network algorithm modules, the proposed framework is able to complete target detection of 1D/2D barcodes in photos or video streams. Faster R-CNN is used for 2D barcode and SSD-ResNet for 1D barcode. Then the target barcode and the coordinates of the barcode are intercepted, the books are sorted by the barcode coordinates, and the values are obtained through the barcode recognition to realize the book inventory. Through inventory test on 1D/2D barcode book, the accuracy and recall rate of the framework reached above 99%, with precision rate close to 100%. Compared with RFID inventory, the proposed deep neural network based inventory method is more accurate and precise, and increased the processing speed by around 35%.