scholarly journals Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 36612-36623 ◽  
Author(s):  
Muhammad Junaid ◽  
Mubeen Ghafoor ◽  
Ali Hassan ◽  
Shehzad Khalid ◽  
Syed Ali Tariq ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Road Segmentation

scholarly journals Review of Road Detection and Segmentation Technique Using CNN

2020 ◽  
pp. 161-165
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Aerial Images ◽  
Road Detection ◽  
Insurance Plan ◽  
Toll Road ◽  
White Pixel ◽  
Road Segmentation ◽  
Matlab Programming ◽  
The Relationship

This research proposes form shape mounted on “the deep convolutional neural network (CNN) for the detection of roads and the segmentation of aerial pix. Those images are received by using a UAV. The photograph segmentation set of rules has two levels: the studying segment and the working phase. The aerial images of the data deteriorated into their coloration additives, had been pre-processed in matlab on hue, after which divided into small 33 × 33 pixel packing containers the usage of a sliding container set of rules. CNN was once designed with matconvnet and had the accompanying structure: 4 convolutional levels, 4 grouping stages, a relu layer, a totally linked layer, and a softmax layer. The entire community has been organized for the use of 2,000 boxes. CNN was implemented the use of matlab programming on the gpu and the outcomes are promising. The CNN output offers pixel-by means of-pixel records, which class it has a location with (road / non-road). White pixel and choppy terrain are known as "0" (dark). Monitoring roads is a troublesome venture in aerial picture segmentation due to quite more than a few sizes and surfaces. One of the vastest steps in CNN training is the pre-processing phase. Due to toll road segmentation, dismissal structures and complexity enhancement have been applied.” this is an audited article on the relationship between representative upkeep techniques with work pleasure and responsibility in insurance plan businesses.

scholarly journals Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4711 ◽  
Author(s):  
Kewei Wang ◽  
Fuwu Yan ◽  
Bin Zou ◽  
Luqi Tang ◽  
Quan Yuan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Dynamic Scenes ◽  
Comprehensive Understanding ◽  
Global Context ◽  
The Road ◽  
Semantic Domain ◽  
Road Segmentation

The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene. To this end, we create a small but effective dataset based on the KITTI dataset named KITTI-OFRS (KITTI-occlusion-free road segmentation) dataset and propose a lightweight and efficient, fully convolutional neural network called OFRSNet (occlusion-free road segmentation network) that learns to predict occluded portions of the road in the semantic domain by looking around foreground objects and visible road layout. In particular, the global context module is used to build up the down-sampling and joint context up-sampling block in our network, which promotes the performance of the network. Moreover, a spatially-weighted cross-entropy loss is designed to significantly increases the accuracy of this task. Extensive experiments on different datasets verify the effectiveness of the proposed approach, and comparisons with current excellent methods show that the proposed method outperforms the baseline models by obtaining a better trade-off between accuracy and runtime, which makes our approach is able to be applied to autonomous vehicles in real-time.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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