DFPNet:Dislocation Double Feature Pyramid Real-time Semantic Segmentation Network

Autonomous harvesting shows a promising prospect in the future development of theagriculture industry, while the vision system is one of the most challenging components in theautonomous harvesting technologies. This work proposes a multi-function network to perform thereal-time detection and semantic segmentation of apples and branches in orchard environments byusing the visual sensor. The developed detection and segmentation network utilises the atrous spatialpyramid pooling and the gate feature pyramid network to enhance feature extraction ability of thenetwork. To improve the real-time computation performance of the network model, a lightweightbackbone network based on the residual network architecture is developed. From the experimentalresults, the detection and segmentation network with ResNet-101 backbone outperformed on thedetection and segmentation tasks, achieving an F1 score of 0.832 on the detection of apples and 87.6%and 77.2% on the semantic segmentation of apples and branches, respectively. The network modelwith lightweight backbone showed the best computation efficiency in the results. It achieved an F1score of 0.827 on the detection of apples and 86.5% and 75.7% on the segmentation of apples andbranches, respectively. The weights size and computation time of the network model with lightweightbackbone were 12.8 M and 32 ms, respectively. The experimental results show that the detection andsegmentation network can effectively perform the real-time detection and segmentation of applesand branches in orchards.

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CFPNET: Channel-Wise Feature Pyramid For Real-Time Semantic Segmentation

10.1109/icip42928.2021.9506485 ◽

2021 ◽

Author(s):

Ange Lou ◽

Murray Loew

Keyword(s):

Real Time ◽

Semantic Segmentation ◽

Feature Pyramid

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FPANet: Feature pyramid aggregation network for real-time semantic segmentation

Applied Intelligence ◽

10.1007/s10489-021-02603-z ◽

2021 ◽

Author(s):

Yun Wu ◽

Jianyong Jiang ◽

Zimeng Huang ◽

Youliang Tian

Keyword(s):

Real Time ◽

Semantic Segmentation ◽

Feature Pyramid

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Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽

10.21820/23987073.2020.2.9 ◽

2020 ◽

Vol 2020 (2) ◽

pp. 9-11

Author(s):

Tomohiro Fukuda

Keyword(s):

Deep Learning ◽

Real Time ◽

Computer Games ◽

Construction Projects ◽

Mixed Reality ◽

Semantic Segmentation ◽

Environment Design ◽

Aviation Training ◽

Architecture And Design ◽

World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

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A lightweight network with attention decoder for real-time semantic segmentation

The Visual Computer ◽

10.1007/s00371-021-02115-4 ◽

2021 ◽

Author(s):

Kang Wang ◽

Jinfu Yang ◽

Shuai Yuan ◽

Mingai Li

Keyword(s):

Real Time ◽

Semantic Segmentation

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Real-time 2D–3D door detection and state classification on a low-power device

SN Applied Sciences ◽

10.1007/s42452-021-04588-3 ◽

2021 ◽

Vol 3 (5) ◽

Author(s):

João Gaspar Ramôa ◽

Vasco Lopes ◽

Luís A. Alexandre ◽

S. Mogo

Keyword(s):

Low Power ◽

Real Time ◽

Object Classification ◽

Semantic Segmentation ◽

Detection Algorithm ◽

Power Device ◽

Indoor Environments ◽

State Classification ◽

Segmentation Algorithms ◽

Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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