Ocean Science and Technology School, Korea Maritime and Ocean University, Busan 49112, Korea.
Maritime ICT R&D Center, Korea Institute of Ocean Science & Technology, Busan 49111, Korea.
Sensors (Basel). 2022 Nov 3;22(21):8445. doi: 10.3390/s22218445.
Unmanned underwater operations using remotely operated vehicles or unmanned surface vehicles are increasing in recent times, and this guarantees human safety and work efficiency. Optical cameras and multi-beam sonars are generally used as imaging sensors in underwater environments. However, the obtained underwater images are difficult to understand intuitively, owing to noise and distortion. In this study, we developed an optical and sonar image fusion system that integrates the color and distance information from two different images. The enhanced optical and sonar images were fused using calibrated transformation matrices, and the underwater image quality measure (UIQM) and underwater color image quality evaluation (UCIQE) were used as metrics to evaluate the performance of the proposed system. Compared with the original underwater image, image fusion increased the mean UIQM and UCIQE by 94% and 27%, respectively. The contrast-to-noise ratio was increased six times after applying the median filter and gamma correction. The fused image in sonar image coordinates showed qualitatively good spatial agreement and the average IoU was 75% between the optical and sonar pixels in the fused images. The optical-sonar fusion system will help to visualize and understand well underwater situations with color and distance information for unmanned works.
近年来，使用遥控水下机器人或无人水面艇进行的水下无人作业越来越多，这保证了人员的安全和工作效率。光学摄像机和多波束声纳通常用作水下环境中的成像传感器。然而，由于噪声和失真，获得的水下图像难以直观理解。在本研究中，我们开发了一种光学和声纳图像融合系统，该系统集成了两个不同图像的颜色和距离信息。使用校准的变换矩阵融合增强的光学和声纳图像，并使用水下图像质量度量 (UIQM) 和水下彩色图像质量评估 (UCIQE) 作为指标来评估所提出系统的性能。与原始水下图像相比，图像融合分别将平均 UIQM 和 UCIQE 提高了 94%和 27%。应用中值滤波和伽马校正后，对比度噪声比提高了六倍。在声纳图像坐标中，融合后的图像在空间上具有很好的一致性，并且在融合图像中的光学和声纳像素之间的平均 IoU 为 75%。光学-声纳融合系统将有助于对具有颜色和距离信息的水下情况进行可视化和理解，以便进行无人作业。
