Coping with Changing Articles in Automated Picking Systems by Adaptive Object Detection and Human-Robot Cooperation

2021 ◽  
Author(s):  
Richard Verbeet ◽  
Mathias Rieder ◽  
Norbert Bartneck
Keyword(s):  
Object Detection ◽  
Picking Systems ◽  
Robot Cooperation

ROBUST 3-D OBJECT DETECTION AND FEATURE EXTRACTION FOR MULTI-ROBOT COOPERATION

2011 ◽  
Vol 08 (01) ◽  
pp. 17-38
Author(s):  
ARUNASIRI K. LIYANAGE ◽  
CLARENCE W. DE SILVA
Keyword(s):  
Object Detection ◽  
Vision System ◽  
Detection Methods ◽  
Computer Vision System ◽  
New Techniques ◽  
Emergency Scenario ◽  
Unstructured Environment ◽  
Different Types ◽  
Robot Cooperation ◽  
Multi Robot

This paper develops a computer vision system for multi-robot cooperation in rescue operations. The work is focused on providing robust and fast vision capabilities to the robots to meet the expected level of performance. Enhanced versions of existing techniques and new techniques are utilized to develop an adaptive vision system architecture for use in an unstructured environment of multi-robot activity in an emergency scenario. Different types of object detection methods are selected in real time in the developed system according to the requirements of a robot. To validate the developed system for use in a multi-robot application, rigorous experiments are conducted in a typical unstructured environment. Features such as invariance of scale, rotation, illumination, and occlusion are tested with different types of objects, for various methods. Generally, good results are obtained.

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

Object Detection in Cluttered Range Images Using Edgel Geometry

2010 ◽  
Vol 130 (9) ◽  
pp. 1572-1580
Author(s):  
Dipankar Das ◽  
Yoshinori Kobayashi ◽  
Yoshinori Kuno
Keyword(s):  
Object Detection ◽  
Range Images

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

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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