现代防御技术2024，Vol.52Issue(3)：104-111,8.DOI:10.3969/j.issn.1009-086x.2024.03.013
带并行卷积的水下图像背景前景分割方法
Underwater Image Background and Foreground Segmentation Method with Parallel Convolution
涂拥军 1林鸿生 1王智勇2
作者信息
- 1. 海军士官学校,安徽蚌埠 233012
- 2. 中国人民解放军92682部队,广东湛江 524003
- 折叠
摘要
Abstract
Optical detection is an essential tool for underwater detection at short range,while underwater optical images are ineffective for underwater applications due to their low signal-to-noise ratio,low contrast and non-uniform illumination.A common method of underwater optical image processing is background foreground segmentation of the image.There are two main types of methods for performing image segmentation:traditional segmentation methods and deep learning methods.Traditional segmentation methods are susceptible to poor segmentation due to illumination and noise,while common deep learning methods are susceptible to training data limitations and poor generalization.In this paper,a neural network structure with parallel convolution and a constrained loss function are designed,and the optimal values of the hyper-parameters of the loss function are obtained through extensive experiments.The results show that the MAE values of the experimental results obtained by this paper are much smaller than those of FCN8 and UNet,and the mIoU values are larger than those of FCN8 and Unet,and the P-R curves are better than those of other methods,which are more adaptable to the complex and variable characteristics of the underwater environment and can obtain better segmentation results.关键词
阈值分割/水下图像/光照不均/并行卷积/深度学习Key words
threshold segmentation/underwater image/non-uniform illumination/parallel convolution/deep learning分类
信息技术与安全科学引用本文复制引用
涂拥军,林鸿生,王智勇..带并行卷积的水下图像背景前景分割方法[J].现代防御技术,2024,52(3):104-111,8.
