数字农科院2.0

Efficient Dual-Branch Bottleneck Networks of Semantic Segmentation Based on CCD Camera

文献类型： 外文期刊

作者： Li, Jiehao;Dai, Yingpeng;Su, Xiaohang;Wu, Weibin

作者机构：

关键词： semantic segmentation;CCD camera;lightweight network;neural networks

期刊名称： REMOTE SENSING

ISSN：

年卷期： 2022 年 14.0 卷 16 期

页码：

摘要： This paper investigates a novel Efficient Dual-branch Bottleneck Network (EDBNet) to perform real-time semantic segmentation tasks on mobile robot systems based on CCD camera. To remedy the non-linear connection between the input and the output, a small-scale and shallow module called the Efficient Dual-branch Bottleneck (EDB) module is established. The EDB unit consists of two branches with different dilation rates, and each branch widens the non-linear layers. This module helps to simultaneously extract local and situational information while maintaining a minimal set of parameters. Moreover, the EDBNet, which is built on the EDB unit, is intended to enhance accuracy, inference speed, and parameter flexibility. It employs dilated convolution with a high dilation rate to increase the receptive field and three downsampling procedures to maintain feature maps with superior spatial resolution. Additionally, the EDBNet uses effective convolutions and compresses the network layer to reduce computational complexity, which is an efficient technique to capture a great deal of information while keeping a rapid computing speed. Finally, using the CamVid and Cityscapes datasets, we obtain Mean Intersection over Union (MIoU) results of 68.58 percent and 71.21 percent, respectively, with just 1.03 million parameters and faster performance on a single GTX 1070Ti card. These results also demonstrate the effectiveness of the practical mobile robot system.

分类号：

  • 相关文献

[1]Semantic Category Balance-Aware Involved Anti-Interference Network for Remote Sensing Semantic Segmentation. Nie, Jie,Wang, Zhaoxin,Liang, Xinyue,Yang, Chenxue,Zheng, Chengyu,Wei, Zhiqiang. 2023

[2]Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees. Gao, Ang,Tian, Zhiwei,Ma, Wei,Song, Yuepeng,Ren, Longlong,Feng, Yuliang,Qian, Jianping,Xu, Lijia. 2024

[3]Modeling greenhouse air humidity by means of artificial neural network and principal component analysis. He, Fen,Ma, Chengwei,He, Fen.

[4]Short-Term Price Forecasting For Agro-products Using Artificial Neural Networks. Li, Gan-qiong,Xu, Shi-wei,Li, Zhe-min. 2010

[5]DAFFnet: Seed classification of soybean variety based on dual attention feature fusion networks. Lingyu Zhang,Laijun Sun,Xiuliang Jin,Xiangguang Zhao,Shujia Li. 2025

[6]Forecasting provincial agricultural output value in China via multiple nighttime light indices and neural networks. Rongchao Yang,Qingbo Zhou,Lei Xu,Zijuan Zhao,Yi Zhang,Tongyang Wei. 2025

[7]Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning. Jiang, Fan,Peng, Peng,Xu, Zhenting,Xu, Yu,Yang, Ding,Chai, Shouquan,Yuan, Shuai,Hua, Limin,Wang, Dawei,Wen, Xuanye. 2025

[8]Multi-Scale Depthwise Separable Convolution for Semantic Segmentation in Street–Road Scenes. Yingpeng Dai,Chenglin Li,Xiaohang Su,Hongxian Liu,Jiehao Li. 2023

[9]TMNet: A Two-Branch Multi-Scale Semantic Segmentation Network for Remote Sensing Images. Yupeng Gao,Shengwei Zhang,Dongshi Zuo,Weihong Yan,Xin Pan. 2023

[10]Study of convolutional neural network-based semantic segmentation methods on edge intelligence devices for field agricultural robot navigation line extraction. Jiya Yu,Jiye Zhang,Aijing Shu,Yujie Chen,Jianneng Chen,Yongjie Yang,Wei Tang,Yanchao Zhang. 2023

[11]Improved 3D point cloud segmentation for accurate phenotypic analysis of cabbage plants using deep learning and clustering algorithms. Ruichao Guo,Jilong Xie,Jiaxi Zhu,Ruifeng Cheng,Yi Zhang,Xihai Zhang,Xinjing Gong,Ruwen Zhang,Hao Wang,Fanfeng Meng. 2023

[12]Deriving Agricultural Field Boundaries for Crop Management from Satellite Images Using Semantic Feature Pyramid Network. Yang Xu,Xinyu Xue,Zhu Sun,Wei Gu,Longfei Cui,Yongkui Jin,Yubin Lan. 2023

[13]Attention-optimized DeepLab V3+for automatic estimation of cucumber disease severity. Li, Kaiyu,Zhang, Lingxian,Li, Bo,Li, Shufei,Ma, Juncheng. 2022

[14]MRUNet: A two-stage segmentation model for small insect targets in complex environments. Fu kuan WANG,Yi qi HUANG,Zhao cheng HUANG,Hao SHEN,Cong HUANG,Xi QIAO,Wan qiang QIAN. 2023

[15]Detection and Mapping of Chestnut Using Deep Learning from High-Resolution UAV-Based RGB Imagery. Yifei Sun,Zhenbang Hao,Zhanbao Guo,Zhenhu Liu,Jiaxing Huang. 2023

[16]Lightweight multi-scale feature dense cascade neural network for scene understanding of intelligent autonomous platform. Dai, Yingpeng,Meng, Lingfeng,Sun, Fushan,Wang, Songfeng. 2024

[17]Semantic segmentation of remote sensing images based on multiscale features and global information modeling. Yupeng Gao,Xiaoling Luo,Xiaojing Gao,Weihong Yan,Xin Pan,Xueliang Fu. 2024

[18]Intelligent weight prediction of cows based on semantic segmentation and back propagation neural network. Beibei Xu,Yifan Mao,Wensheng Wang,Guipeng Chen. 2024

[19]Crop Land Change Detection with MC&N-PSPNet. Yuxin Chen,Yulin Duan,Wen Zhang,Chang Wang,Qiangyi Yu,Xu Wang. 2024

[20]A Recognition Model Based on Multiscale Feature Fusion for Needle-Shaped Bidens L. Seeds. Zizhao Zhang,Yiqi Huang,Ying Chen,Ze Liu,Bo Liu,Conghui Liu,Cong Huang,Wanqiang Qian,Shuo Zhang,Xi Qiao. 2025

作者其他论文 更多>>