A Study of Spatial Attention and Squeeze Excitation Block Fusion Improved ResNet for Identifying Bank Notes

Abstract

Based on deep learning and digital image processing algorithms, we design and implement an accurate automatic recognition system for bank note text and propose an improved recognition method based on ResNet for the problems of difficult image text extraction and insufficient recognition accuracy. Firstly, a deep hyperparameterized convolution (DO-Conv) is used instead of the traditional convolution in the network to improve the recognition rate while reducing the model parameters. Then, the spatial attention model (SAM) and the squeezed excitation block (SE-Block) are fused and applied to a modified ResNet to extract detailed features of bank note images in the channel and spatial domains. Finally, the label-smoothed cross-entropy (LSCE) loss function is used to train the model to automatically calibrate the network to prevent classification errors. The experimental results demonstrate that the improved model is not easily affected by the image quality, and the model in this paper has good performance in text detection and recognition in specific business ticket scenarios.

Hardware acceleration for real-time vehicle object detection based on MPSoC *

“…The image target detection method based on deep learning technology mainly relies on Convolutional Neural Networks (CNN) [2] to automatically extract image features and perform target recognition.From 2013 to the present, with the increase in computing power and the availability of massive data, CNN has made significant progress in the field of vehicle object recognition. During this period, various network structures such as VGGNet [3] , GoogLeNet [4] , ResNet [5] , and detection algorithms like R-CNN, YOLO, SSD, etc. have emerged, achieving important breakthroughs in improving the accuracy and speed of vehicle object recognition.…”

Retracted: A Study of Spatial Attention and Squeeze Excitation Block Fusion Improved ResNet for Identifying Bank Notes

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”