Effective Meta-Attention Dehazing Networks for Vision-Based Outdoor Industrial Systems
Effective Meta-Attention Dehazing Networks for Vision-Based Outdoor Industrial Systems
复制标题
用于基于视觉的户外工业系统的有效元注意力去雾网络
DOI：
10.1109/tii.2021.3059020
复制
发表时间：
2021-02
期刊：
影响因子：
--
通讯作者：
Guoqiang Li
中科院分区：
文献类型：
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作者：
Tongyao Jia;Jiafeng Li;Li Zhuo;Guoqiang Li
雾霾严重影响工业系统的可靠性，尤其是自动驾驶系统等基于视觉的户外工业系统。大多数现有的去雾方法并不是专门针对工业系统设计的，也没有考虑工业系统实施的可靠性和资源成本。在本文中，提出了一种新颖的元注意去雾网络（MADN），用于在不使用物理散射模型的情况下直接从模糊图像恢复清晰图像。结合并行操作和增强模块，元网络通过元注意力模块根据当前输入的模糊图像自动选择最合适的去雾网络结构。此外，提出了一种由元网络计算的新颖特征损失，可以加速去雾网络的收敛，以满足实际工业系统的应用需求。在合成数据集和真实数据集上的大量实验结果表明，所提出的 MADN 满足工业系统的需求。
Haze seriously affects the reliability of industrial systems, especially vision-based outdoor industrial systems such as autopilot systems. A majority of existing dehazing methods are not specifically designed for industrial systems and do not consider the reliability and resource cost of industrial system implementation. In this article, a novel meta-attention dehazing network (MADN) is proposed for direct restoration of clear images from hazy images without using the physical scattering model. Combined with parallel operation and enhancement modules, the meta-network automatically selects the most suitable dehazing network structure based on the current input hazy image by a meta-attention module. In addition, a novel feature loss calculated by the meta-network is proposed, which can accelerate the convergence of the dehazing network to meet the application requirements of practical industrial systems. A large number of experimental results on synthetic and real-world datasets show that the proposed MADN satisfies the needs of industrial systems.