基于图像风格转换的水下图像显著性检测算法

Underwater Image Salient Object Detection Algorithm Based on Image Style Transfer

由于水下显著性检测数据集不足,导致基于深度学习的水下图像显著性检测网络容易出现过拟合的问题,从而影响显著性检测网络的性能。针对上述问题,本文引入图像风格转换方法,提出一种基于CycleGAN的水下显著性检测网络。网络生成器由图像风格转换子网络和显著性检测子网络构成。首先,通过无监督的级联方式对风格转换子网络进行风格转换训练,并利用该网络对陆地图像和水下图像进行风格转换,构建训练和测试图像数据集,以解决水下显著性检测数据集不足的问题;然后,使用陆地及其风格转换后的显著性数据集对显著性检测子网络进行训练,以增强网络的特征提取能力;最后对两个图像风格的输出结果进行融合优化,以提高显著性检测网络性能。实验结果表明,本文提出的水下显著性检测网络相比于单纯的陆地和水下图像显著性检测网络,其检测平均绝对误差和F值至少分别提高了10.4%和2.4%。

Due to the insufficient underwater salient object detection datasets,the underwater image salient object detection network based on deep learning is prone to overfitting,which affects the performance of the network.In response to the above problems,this paper introduces an image style conversion method and proposes an underwater salient object detection network based on CycleGAN.The network generator is composed of an image style conversion subnetwork and a salient object detection subnetwork.First,the network trains the domain transform subnetwork through unsupervised cascade method,and uses the network to preform style transform on in-air and underwater images to construct training and testing datasets,so as to solve the insufficient problem of underwater salient object detection.Then,it uses in-air and salient object detection datasets after style transformation to train the salient object detection subnetwork to enhance the feature extraction ability of the network.Finally,the output results of the two image styles are fused and optimized to improve the performance of the saliency detection network.Experimental results show that compared with the land and underwater salient object detection network,the mean average error(MAE)and F-measure are relatively increased at least 10.4%and 2.4%,respectively.