The cat’s eye target recognition based on fully convolutional residual network and visual saliency

Abstract

In order to improve the accuracy and speed of cat-eye target recognition, a cat-eye target recognition method combining fully convolutional residual network and visual saliency is proposed. This method is based on semantic segmentation. Firstly, the collected active and passive images were preprocessed, and saliency detection is used to enhance the cat-eye target in the different image. Second, the active image is segmented by the full convolutional residual network to achieve background suppression and target enhancement, and the fusion result graph containing the cat-eye target region is obtained by the and operation of the two results. Finally, the candidate target area of the cat-eye target Recognition, to extract real cat-eye target. The experimental results show that the proposed method has high accuracy and fast running time, and can be used for real-time detection.