Target recognition based on pre-processing in computational ghost imaging with deep learning

It is important to recognize the target of interest in an imaging area. Target recognition is applicable in fields such as driverless cars and military technology, while target recognition precision is influenced by environmental disturbance. Ghost imaging (GI) presents an anti-disturbance property with potential in target recognition. This paper proposes a characteristic imaging model for target recognition based on preprocessing in computational GI with deep learning. Different from GI trained by detected signals, reference beams pretrained by a deep learning network are used in correlation calculation in our imaging scheme. The imaging object, presenting identical characteristics with the training object, is easier to recognize. Influencing factors in real application scenes are considered, including the size and posture of the imaging object and occlusions, and the results show that our imaging scheme can recognize the target of interest. These results demonstrate potential applications in target recognition.

Automated summary

Recognizing the target of interest in an imaging area is crucial. Ghost imaging (GI) shows potential in target recognition by presenting an anti-disturbance property. This paper proposes a characteristic imaging model for target recognition based on deep learning in computational GI. The results demonstrate the potential applications of this imaging scheme in recognizing the target of interest.