scholarly journals MPFANet: Semantic Segmentation Using Multiple Path Feature Aggregation

2021 ◽  
Vol 21 (4) ◽  
pp. 401-408
Author(s):  
Wang-Su Jeon ◽  
Sang-Yong Rhee
Keyword(s):  
Semantic Segmentation ◽  
Multiple Path ◽  
Feature Aggregation

scholarly journals Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation

2021 ◽  
Author(s):  
Peng Sun ◽  
Jiaxiang Wu ◽  
Songyuan Li ◽  
Peiwen Lin ◽  
Junzhou Huang ◽  
...  
Keyword(s):  
Real Time ◽  
Semantic Segmentation ◽  
Joint Decision ◽  
Feature Aggregation

scholarly journals DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

2021 ◽  
Vol 10 (3) ◽  
pp. 125
Author(s):  
Junqing Huang ◽  
Liguo Weng ◽  
Bingyu Chen ◽  
Min Xia
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Information ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Dual Function ◽  
Context Information ◽  
Remote Sensing Images ◽  
Feature Aggregation ◽  
Image Context

Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%.

scholarly journals Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation

2021 ◽  
Vol 6 (3) ◽  
pp. 5889-5896
Author(s):  
Yechao Bai ◽  
Ziyuan Huang ◽  
Lyuyu Shen ◽  
Hongliang Guo ◽  
Marcelo H. Ang Jr ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Scale Feature ◽  
Multi Scale ◽  
Feature Aggregation

scholarly journals Multi-Level and Multi-Scale Feature Aggregation Network for Semantic Segmentation in Vehicle-Mounted Scenes

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3270
Author(s):  
Yong Liao ◽  
Qiong Liu
Keyword(s):  
Receptive Fields ◽  
Semantic Segmentation ◽  
Layer By Layer ◽  
Scale Feature ◽  
Backbone Networks ◽  
Multi Scale ◽  
Backbone Network ◽  
Feature Aggregation ◽  
Multi Level ◽  
High Level

The main challenges of semantic segmentation in vehicle-mounted scenes are object scale variation and trading off model accuracy and efficiency. Lightweight backbone networks for semantic segmentation usually extract single-scale features layer-by-layer only by using a fixed receptive field. Most modern real-time semantic segmentation networks heavily compromise spatial details when encoding semantics, and sacrifice accuracy for speed. Many improving strategies adopt dilated convolution and add a sub-network, in which either intensive computation or redundant parameters are brought. We propose a multi-level and multi-scale feature aggregation network (MMFANet). A spatial pyramid module is designed by cascading dilated convolutions with different receptive fields to extract multi-scale features layer-by-layer. Subseqently, a lightweight backbone network is built by reducing the feature channel capacity of the module. To improve the accuracy of our network, we design two additional modules to separately capture spatial details and high-level semantics from the backbone network without significantly increasing the computation cost. Comprehensive experimental results show that our model achieves 79.3% MIoU on the Cityscapes test dataset at a speed of 58.5 FPS, and it is more accurate than SwiftNet (75.5% MIoU). Furthermore, the number of parameters of our model is at least 53.38% less than that of other state-of-the-art models.

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