无人艇光视觉感知的轻量型残差堆叠低照度图像增强网络

A lightweight stacked residual low⁃light image enhancement network for USV optical vision perception

针对无人艇在低照度环境中感知困难问题,提出一种轻量型残差堆叠低照度图像增强网络。首先,在特征融合中引入金字塔多尺度池化,以更好地保留图像细节。其次,引入深度可分离卷积以减轻网络负担,提高图像处理速度。再次,设计一种新的复合损失函数,引入颜色损失以减少颜色失真。最后,采用LeakyReLU激活函数防止神经元死亡。实验结果表明,相比残差堆叠注意力低照度增强网络(SARN),本文方法在提升图像质量的同时加快了图像处理速度,其中,结构相似性和峰值信噪比分别提高了3.31%和2.08%,模型计算量、参数量和单张处理时间分别减小了81.88%、75%和43.02%。

A lightweight stacked residual low⁃light image en⁃hancement network was proposed to overcome the difficulty in accurately sensing the environment under low⁃light conditions for unmanned surface vessels(USV).Firstly,a pyramid multi⁃scale pooling was introduced into feature fusion to better preserve image details.Secondly,depthwise separable convo⁃lution was introduced to reduce network burden and improve the image processing speed.Thirdly,a new composite loss function with color loss was designed to reduce color distor⁃tion.Finally,LeakyReLU activation function was used to pre⁃vent neuronal death.Results show that compared to the low il⁃lumination image enhancement network of stacked attention residual network(SARN),the proposed method improves im⁃age quality while accelerating image processing speed.The structural similarity and peak signal⁃to⁃noise ratio are im⁃proved by 3.31%and 2.08%,respectively.The model com⁃putation,parameter count,and single image processing time are reduced by 81.88%,75%,and 43.02%,respectively.