一种基于深度学习图像超分的环形靶标稳定检测方法

A Circular Target Stability Detection Method Based on Deep Learning Image Super-resolution

为提高远距离、大倾角条件下环形靶标的识别率与定位精度,提出了一种基于深度学习图像超分的环形靶标稳定检测方法。通过真实图像与合成图像的混合数据集来构建多角度、多距离的图像空间集合,采用像素损失与感知损失来改进图像超分辨率模型的损失函数,从而实现图像的高分辨率重建,丰富靶标轮廓的图像细节,利用已训练好的图像超分模型重建图像,最后使用传统的检测算法识别与定位环形靶标。实验结果表明,环形靶标识别率可提高40%,靶标定位精度可提高8.47%。

In order to improve the recognition rate and location accuracy of circular targets under the conditions of long-distance and large deflection angle,a circular target stability detection method was proposed based on deep learning image super-resolution.The multi-angle and multi-distance image sets were constructed through a hybrid data set of real images and synthetic images,the pixel loss and perceptual loss were used to improve the loss function of image super-resolution model,so the super-resolution reconstruction of images might be realized and the image details of target contours might be enriched.By using the pretrained super-resolution model,the images were reconstructed.Finally,traditional detection algorithm was used to recognize and locate the circular targets.The experimental results show that the circular target recognition rate is increased by 40%,and the target location accuracy is increased by 8.47%.