The Cat's Eye Effect Target Recognition Method Based on deep convolutional neural network

Laser active detection technology based on the "cat's eye effect" is becoming more and more important in the fields of photoelectric reconnaissance and tracking. It is an effective means for identifying and interfering with photoelectric reconnaissance targets. In order to improve the accuracy and detection speed of cat-eye effect target recognition, this paper proposes a cat-eye effect target recognition method based on deep convolutional neural network. In the process of identifying cat-eye targets: preprocess the detected active and passive images to find candidate target regions, use classification network to screen the candidate target regions, and finally identify cat-eye effect targets. The experiment verifies the advantages of this method over the traditional cat-eye effect target recognition algorithm. The proposed method has high accuracy, fast recognition speed, and can be used for real-time detection.