Affiliation:
1. College of Information Engineering, North China University of Water Resources and Electric Power 1 , Zhengzhou 450000, China
2. College of Electrical Engineering, North China University of Water Resources and Electric Power 2 , Zhengzhou 450000, China
Abstract
A network model based on the self-attention mechanism is proposed to address the difficulties in extracting features from ghost imaging targets, low recognition efficiency, and potential errors. First, a ghost imaging detection system is constructed using a laser, spatial light modulator, bucket detector, etc. The object is illuminated with speckles generated by the spatial light modulator. The detected data are then input into the self-attention mechanism network model for training. Experimental results show that for the handwritten digits in the experimental dataset, the highest accuracy and average accuracy of the self-attention mechanism network are 99.13% and 96.41%, respectively. This experiment demonstrates the potential of using the self-attention mechanism network for target recognition in ghost imaging, improving the speed of target recognition and significantly enhancing the accuracy of recognition.
Funder
National Natural Science Foundation of China
Subject
General Physics and Astronomy