Sea–sky lines (SKLs) can provide valuable reference information for the obstacle avoidance systems of unmanned surface vehicles (USVs) because obstacles that can threaten the safety of USVs, such as ships and rocks, are generally located below SKLs. Existing methods detect SKLs only using gray, texture or line features in an optical image. However, the background features in images obtained by onboard cameras are complex and change continuously over time, which leads to poor robustness of these methods. Conversely, the method that we proposed in this paper detects SKLs according to the gray variation differences in the time domain between the sky, the SKL and the sea surface when the USVs actually move on the sea. In this way, we can decrease the gray complexity of the image data of different backgrounds, which reduces interference while enhancing the edge features of the SKLs to obtain superior robustness. The proposed method is tested on optical image sequences collected by the ‘Jiu Hang 490’ USV. The experimental results demonstrate that the average accuracy of SKL detection is higher than that of existing methods with approximately 10% improvement while maintaining computational efficiency.

中文翻译：

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基于时域灰度变化差异的无人水面舰艇海天线检测

海天线（SKLs）可以为无人水面舰艇（USV）的避障系统提供有价值的参考信息，因为可能威胁USV安全的障碍物，如船舶和岩石，通常位于SKLs下方。现有方法仅使用光学图像中的灰度、纹理或线特征来检测 SKL。然而，机载相机获得的图像中的背景特征复杂且随时间不断变化，导致这些方法的鲁棒性较差。相反，我们在本文中提出的方法是根据USV实际在海上移动时天空、SKL和海面之间的时域灰度变化差异来检测SKL。这样我们就可以降低不同背景图像数据的灰度复杂度，这减少了干扰，同时增强了 SKL 的边缘特征以获得卓越的鲁棒性。所提出的方法在'九航490'USV收集的光学图像序列上进行了测试。实验结果表明，在保持计算效率的同时，SKL检测的平均准确率高于现有方法，提高了约10%。