基于循环一致性对抗网络的数码迷彩伪装生成方法

Digital camouflage generation method based on cycle-consistent adversarial network

针对传统的数码迷彩生成方法无法根据背景实时生成数码迷彩的问题,提出一种基于循环一致性对抗网络的数码迷彩生成方法。首先,使用密集连接卷积网络提取图像特征,将学习到的数码迷彩特征映射到背景图像中;其次,加入颜色保持损失来提高数码迷彩的生成质量,保证生成的数码迷彩与周围的背景颜色相一致;最后,在判别器中加入自归一化神经网络以提高模型对噪声的鲁棒性。由于缺乏数码迷彩伪装效果的客观评价标准,采用边缘检测算法与结构相似性(SSIM)算法对生成的数码迷彩的伪装效果进行评估。实验结果表明,该方法在自制数据集上生成的数码迷彩伪装的SSIM得分比已有算法的得分降低了30%以上,验证了它在数码迷彩生成任务上的有效性。

Traditional methods of generating digital camouflages cannot generate digital camouflages based on the background information in real time.In order to cope with this problem,a digital camouflage generation method based on cycle-consistent adversarial network was proposed.Firstly,the image features were extracted by using densely connected convolutional network,and the learned digital camouflage features were mapped into the background image.Secondly,the color retention loss was added to improve the quality of generated digital camouflages,ensuring that the generated digital camouflages were consistent with the surrounding background colors.Finally,a self-normalized neural network was added to the discriminator to improve the robustness of the model against noise.For the lack of objective evaluation criteria for digital camouflages,the edge detection algorithm and the Structural SIMilarity(SSIM)algorithm were used to evaluate the camouflage effects of the generated digital camouflages.Experimental results show that the SSIM score of the digital camouflage generated by the proposed method on the self-made datasets is reduced by more than 30%compared with the existing algorithms,verifying the effectiveness of the proposed method in the digital camouflage generation task.