3D Ship Shape Inspection Approach using Laser Vision Systems for Automatic Ship Loader

2013 ◽  
Vol 5 (7) ◽  
pp. 795-802
Author(s):  
MI Chao ◽  
LIU Haiwei ◽  
ZHAO Ning ◽  
SHEN Yang
Keyword(s):  
Vision Systems ◽  
Laser Vision ◽  
Shape Inspection

Prospects for using gated lidars in autonomous mobile robots

2021 ◽  
Vol 9 (2) ◽  
pp. 133-141
Author(s):  
Valeriy Alexeev ◽  
Dmitry Goryachkin ◽  
Nikolay Gryaznov ◽  
Viktor Kuprenyuk ◽  
Evgeniy Sosnov
Keyword(s):  
High Speed ◽  
Vision System ◽  
Signal To Noise Ratio ◽  
Image Intensifier ◽  
Weather Conditions ◽  
Scanning System ◽  
Vision Systems ◽  
Observation Distance ◽  
Useful Signal ◽  
Laser Vision

The analysis of the implementation problems of technical vision systems based on the use of time-of-flight laser lidars is carried out. It is concluded that the implementation of vision systems with acceptable parameters dictates an excessively high cost of the lidar. An alternative version of the lidar implementation is considered – a gated lidar based on a laser vision system. Replacing the broadband detector and high-speed scanning system with a gated CCD-matrix can significantly reduce the cost of the lidar while ensuring the high resolution of the lidar. The analysis of the dependence of the signal-to-noise ratio for gated lidar with and without an electron-optical converter has shown that in bad weather conditions the decrease in the gain of the useful signal when the image intensifier is excluded is compensated by the exclusion of the EOC's noise factor, so that the loss in the observation distance is less than 15%.

Laser vision plugs gap

Nature ◽  
2002 ◽  
Author(s):  
Philip Ball
Keyword(s):  
Laser Vision

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

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