Sonar Image Translation Using Generative Adversarial Network for Underwater Object Recognition
Sonar Image Translation Using Generative Adversarial Network for Underwater Object Recognition
复制标题
使用生成对抗网络进行水下物体识别的声纳图像翻译
DOI：
10.1109/ut.2019.8734466
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Son
中科院分区：
文献类型：
--
作者：
Minsung Sung;Hyeonwoo Cho;Jason Kim;Son
声纳传感器广泛应用于水下目标识别。然而，获取每个目标对象的参考声纳图像是高成本和耗时的。声纳图像模拟器可以以较小的计算量生成参考声纳图像，但模拟图像与现场实际采集的声纳图像存在差异。本文提出了一种使用生成对抗网络将实际声纳图像转换为模拟图像的方法。我们用室内水箱测试捕获的图像训练了网络。训练后的神经网络可以从给定的实际声纳图像生成类似模拟器的图像。此外，我们可以识别目标对象之间的模板匹配的翻译图像和参考图像模拟的目标对象。
Sonar sensor is widely used for underwater object recognition. However, acquiring reference sonar images for each target object is high-cost and time-consuming. Sonar image simulators can generate reference sonar images with small computation, but the simulated images are different with actual sonar images captured in the field. This paper proposes a method to translate actual sonar images to simulated-like images using a generative adversarial network. We trained the network with images captured by the indoor water tank test. The trained neural network can generate simulator-like images from given actual sonar images. Further, we can recognize the target object using template matching between the translated image and the reference images simulating the target object.