Abstract
Image segmentation plays an important role in the sensing systems of autonomous underwater vehicles for fishing. Via accurately perceiving the marine organisms and surrounding environment, the automatic catch of marine products can be implemented. However, existing segmentation methods cannot precisely segment marine animals due to the low quality and complex shapes of collected marine images in the underwater situation. A novel multi-scale transformer network (MulTNet) is proposed for improving the segmentation accuracy of marine animals, and it simultaneously possesses the merits of a convolutional neural network (CNN) and a transformer. To alleviate the computational burden of the proposed network, a dimensionality reduction CNN module (DRCM) based on progressive downsampling is first designed to fully extract the low-level features, and then they are fed into a proposed multi-scale transformer module (MTM). For capturing the rich contextural information from different subregions and scales, four parallel small-scale encoder layers with different heads are constructed, and then they are combined with a large-scale transformer layer to form a multi-scale transformer module. The comparative results demonstrate MulTNet outperforms the existing advanced image segmentation networks, with MIOU improvements of 0.76% in the marine animal dataset and 0.29% in the ISIC 2018 dataset. Consequently, the proposed method has important application value for segmenting underwater images.
Funder
National Natural Science Foundation of China
Chongqing Research Program of Basic Research and Frontier Exploration
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference57 articles.
1. Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method
2. Marine Animal Detection and Recognition with Advanced Deep Learning Models;Zhuang,2017
3. Marine animal classification using combined CNN and hand-designed image features;Cao;Proceedings of the OCEANS 2015—MTS/IEEE Washington,2015
4. Fully convolutional networks for semantic segmentation;Long;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015
5. Multipath Fusion Mask R-CNN With Double Attention and Its Application Into Gear Pitting Detection
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献