Zai Wenjiao, Yan Lisha
College of Engineering, Sichuan Normal University, Chengdu 610101, China.
Sensors (Basel). 2023 Aug 8;23(16):7026. doi: 10.3390/s23167026.
Unmanned Aerial Vehicle (UAV) inspection of transmission channels in mountainous areas is susceptible to non-homogeneous fog, such as up-slope fog and advection fog, which causes crucial portions of transmission lines or towers to become fuzzy or even wholly concealed. This paper presents a Dual Attention Level Feature Fusion Multi-Patch Hierarchical Network (DAMPHN) for single image defogging to address the bad quality of cross-level feature fusion in Fast Deep Multi-Patch Hierarchical Networks (FDMPHN). Compared with FDMPHN before improvement, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) of DAMPHN are increased by 0.3 dB and 0.011 on average, and the Average Processing Time (APT) of a single picture is shortened by 11%. Additionally, compared with the other three excellent defogging methods, the PSNR and SSIM values DAMPHN are increased by 1.75 dB and 0.022 on average. Then, to mimic non-homogeneous fog, we combine the single picture depth information with 3D Berlin noise to create the UAV-HAZE dataset, which is used in the field of UAV power assessment. The experiment demonstrates that DAMPHN offers excellent defogging results and is competitive in no-reference and full-reference assessment indices.
无人机对山区输电线路进行巡检时，容易受到上坡雾、平流雾等非均匀雾的影响，导致输电线路或杆塔的关键部分变得模糊甚至完全被遮挡。本文提出了一种用于单图像去雾的双注意力层级特征融合多块分层网络（DAMPHN），以解决快速深度多块分层网络（FDMPHN）中跨层级特征融合质量不佳的问题。与改进前的FDMPHN相比，DAMPHN的峰值信噪比（PSNR）和结构相似性指数测量值（SSIM）平均提高了0.3dB和0.011，单张图片的平均处理时间（APT）缩短了11%。此外，与其他三种优秀的去雾方法相比，DAMPHN的PSNR和SSIM值平均提高了1.75dB和0.022。然后，为模拟非均匀雾，我们将单张图片深度信息与3D柏林噪声相结合，创建了无人机-雾霭数据集，该数据集用于无人机电力评估领域。实验表明，DAMPHN具有出色的去雾效果，在无参考和全参考评估指标方面具有竞争力。
