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
刘婷 1张宇欣 1王国峰 1罗佩琪 1范云生1
作者信息
- 1. 大连海事大学船舶电气工程学院,辽宁大连 116026
- 折叠
摘要
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％.
Abstract
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.
关键词
无人艇(USV)/低照度图像增强/卷积神经网络/深度可分离卷积/金字塔池化/颜色损失Key words
unmanned surface vehicle(USV)/low-light im-age enhancement/convolutional neural network/depthwise separable convolution/pyramid pooling/color loss引用本文复制引用
基金项目
中国博士后科学基金资助项目(2019M661076)
出版年
2024
NETL
NSTL
万方数据