DMTN-Net: Semantic Segmentation Architecture for Surface Unmanned Vessels

Aiming at the problems of insufficient navigation area recognition accuracy, fuzzy boundary of obstacle segmentation, and high consumption of computational resources in the autonomous navigation of water navigation sensors, such as USVs, this paper proposes a DMTN-Net network architecture based on D...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) 2024-11, Vol.13 (22), p.4539
Main Authors: Shao, Mingzhi, Liu, Xin, Zhang, Tengwen, Zhang, Qingfa, Sun, Yuhan, Yuan, Haiwen, Xiao, Changshi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Autonomous navigation
Computing costs
Datasets
Decomposition
Deep learning
Efficiency
Obstacle avoidance
Semantic segmentation
Semantics
Sensors
Systems stability
Unmanned vehicles
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aiming at the problems of insufficient navigation area recognition accuracy, fuzzy boundary of obstacle segmentation, and high consumption of computational resources in the autonomous navigation of water navigation sensors, such as USVs, this paper proposes a DMTN-Net network architecture based on DeeplabV3+ to improve the accuracy and efficiency of environment sensing. Firstly, DMTN-Net adopts the lightweight MobileNetV2 as the backbone, which reduces the amount of computation. Secondly, the innovative N-Decoder structure integrates cSE and Triplet Attention, which enhances the feature representation and improves the segmentation performance. Finally, various experiments were conducted on the MassMind dataset, Pascal VOC2007 dataset, and related sea areas. The experimental results show that DMTN-Net performs well on MassMind and Pascal VOC2007 datasets, and compared with other mainstream networks, the indexes of mIoU, mPA, and mPrecision are significantly improved, and the computational cost is greatly reduced. In addition, the offshore navigation experiments further validate its performance advantages and provide solid support for the practicalization of USV waterborne sensors.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13224539