面向水下场景的轻量级图像语义分割网络

Lightweight semantic segmentation network for underwater image

提出面向水下场景的图像语义分割网络,考虑到速度和准确度之间的权衡问题,网络采用轻量且高效的编解码器结构.在编码器部分,设计倒置瓶颈层和金字塔池化模块,高效地提取特征.在解码器部分,构建特征融合模块融合多水平特征,提升了分割的准确度.针对水下图像边缘模糊的问题,使用辅助的边缘损失函数来更好地训练网络,通过语义边界的监督细化分割的边缘.在水下语义分割数据集SUIM上的实验数据表明,对于320像素×256像素的输入图像,该网络在NVIDIA GeForce GTX 1080Ti显卡上的推理速度达到258.94帧/s,mIoU达到53.55%,能够在保证高准确度的同时,达到实时的处理速度.

A semantic segmentation network was designed for underwater images.A lightweight and efficient encoder-decoder architecture was used by considering the trade-off between speed and accuracy.Inverted bottleneck layer and pyramid pooling module were designed in the encoder part to efficiently extract features.Feature fusion module was constructed in the decoder part in order to fuse multi-level features,which improved the segmentation accuracy.Auxiliary edge loss function was used to train the network better aiming at the problem of fuzzy edges of underwater images,and the edges of segmentation were refined through the supervision of semantic boundaries.The experimental data on the underwater semantic segmentation dataset SUIM show that the network achieves 53.55%mean IoU with an inference speed of 258.94 frames per second on one NVIDIA GeForce GTX 1080 Ti card for the input image of pixel 320×256,which can achieve real-time processing speed while maintaining high accuracy.