In sewer pipes, haze caused by the humid environment seriously impairs the quality of closed-circuit television (CCTV) images, which leads to poor performance of subsequent pipe defects detection. Meanwhile, the complexity of sewer images, such as steep depth change and extensive textureless regions, brings great challenges to the performance or application of general dehazing algorithms. Therefore, this study estimates sewer depth maps first with the help of the water–pipewall borderlines to produce the paired dehazing dataset. Then a structure-aware nonlocal network (SANL-Net) is proposed with the detected borderlines and the dehazing result as two supervisory signals. SANL-Net shows its superiority over other state-of-the-art approaches with 147 in mean square error (MSE), 27.28 in peak signal to noise ratio (PSNR), 0.8963 in structural similarity index measure (SSIM), and 15.47M in parameters. Also, the outstanding performance in real image dehazing implies the accuracy of depth estimation. Experimental results indicate that SANL-Net significantly improves the performance of defects detection tasks, such as an increase of 23.16% in mean intersection over union (mIoU) for semantic segmentation.

中文翻译：

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基于单目深度线索的下水道检查图像结构感知去雾

在下水道管道中，潮湿环境造成的雾霾严重影响闭路电视（CCTV）图像质量，导致后续管道缺陷检测效果不佳。同时，下水道图像的复杂性，如陡峭的深度变化和广泛的无纹理区域，给通用去雾算法的性能或应用带来了巨大挑战。因此，本研究首先在水管壁边界线的帮助下估计下水道深度图，以生成成对的去雾数据集。然后提出了一个结构感知的非局部网络（SANL-Net），将检测到的边界线和去雾结果作为两个监督信号。SANL-Net 显示了其优于其他最先进方法的优势，均方误差 (MSE) 为 147，峰值信噪比 (PSNR) 为 27.28，0。结构相似性指数度量（SSIM）为 8963，参数为 15.47M。此外，真实图像去雾的出色表现意味着深度估计的准确性。实验结果表明，SANL-Net 显着提高了缺陷检测任务的性能，例如用于语义分割的平均交叉并集 (mIoU) 提高了 23.16%。