Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387583652> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W4387583652 abstract "As the world moves towards mass customization, there is a need for a manufacturing system that can quickly adapt to market changes. Reconfigurable manufacturing systems (RMS) have been proposed as a solution. RMS is designed to be modular with a high degree of flexibility. However, such a structure creates a lot of complexity. For instance, if the modules are moved or changed, the robot arms in the system must be re-programmed. Adding 3D cameras and image recognition to the robot arms can solve some of these problems. Nevertheless, creating image recognition models is time-consuming work, requires human labor, and can increase the cost of manufacturing. To manufacture a large variety of products, there is a need to create image recognition models for each product. One method to automate the generation of image recognition models can be to use synthetic data. Synthetic data can be used to generate a large amount of labeled data, which can be used to train image recognition models.In this paper, we propose a method for training image recognition models using synthetic data, which can further automate robots in RMS. Specifically, the system utilizes a 3D model of a part to generate images, which are then processed by a cycle generative adversarial network (GAN) to enhance their realism. These images are subsequently auto-labeled and employed to train an image recognition model compatible with an industrial robot arm." @default.
- W4387583652 created "2023-10-13" @default.
- W4387583652 creator A5016397631 @default.
- W4387583652 creator A5018789561 @default.
- W4387583652 creator A5093052722 @default.
- W4387583652 date "2023-09-12" @default.
- W4387583652 modified "2023-10-14" @default.
- W4387583652 title "Towards automatic generation of image recognition models for industrial robot arms" @default.
- W4387583652 cites W2009249904 @default.
- W4387583652 cites W2609731728 @default.
- W4387583652 cites W2773200342 @default.
- W4387583652 cites W2962793481 @default.
- W4387583652 cites W2963037989 @default.
- W4387583652 cites W2980449234 @default.
- W4387583652 cites W3025800305 @default.
- W4387583652 cites W3035756007 @default.
- W4387583652 cites W3045349751 @default.
- W4387583652 cites W3046768359 @default.
- W4387583652 cites W3094127673 @default.
- W4387583652 cites W3200426980 @default.
- W4387583652 cites W3205490848 @default.
- W4387583652 cites W4283802447 @default.
- W4387583652 cites W4292862452 @default.
- W4387583652 cites W4296181642 @default.
- W4387583652 doi "https://doi.org/10.1109/etfa54631.2023.10275616" @default.
- W4387583652 hasPublicationYear "2023" @default.
- W4387583652 type Work @default.
- W4387583652 citedByCount "0" @default.
- W4387583652 crossrefType "proceedings-article" @default.
- W4387583652 hasAuthorship W4387583652A5016397631 @default.
- W4387583652 hasAuthorship W4387583652A5018789561 @default.
- W4387583652 hasAuthorship W4387583652A5093052722 @default.
- W4387583652 hasConcept C101468663 @default.
- W4387583652 hasConcept C105795698 @default.
- W4387583652 hasConcept C111919701 @default.
- W4387583652 hasConcept C115961682 @default.
- W4387583652 hasConcept C154945302 @default.
- W4387583652 hasConcept C2776126113 @default.
- W4387583652 hasConcept C2780598303 @default.
- W4387583652 hasConcept C31972630 @default.
- W4387583652 hasConcept C33923547 @default.
- W4387583652 hasConcept C41008148 @default.
- W4387583652 hasConcept C90509273 @default.
- W4387583652 hasConceptScore W4387583652C101468663 @default.
- W4387583652 hasConceptScore W4387583652C105795698 @default.
- W4387583652 hasConceptScore W4387583652C111919701 @default.
- W4387583652 hasConceptScore W4387583652C115961682 @default.
- W4387583652 hasConceptScore W4387583652C154945302 @default.
- W4387583652 hasConceptScore W4387583652C2776126113 @default.
- W4387583652 hasConceptScore W4387583652C2780598303 @default.
- W4387583652 hasConceptScore W4387583652C31972630 @default.
- W4387583652 hasConceptScore W4387583652C33923547 @default.
- W4387583652 hasConceptScore W4387583652C41008148 @default.
- W4387583652 hasConceptScore W4387583652C90509273 @default.
- W4387583652 hasLocation W43875836521 @default.
- W4387583652 hasOpenAccess W4387583652 @default.
- W4387583652 hasPrimaryLocation W43875836521 @default.
- W4387583652 hasRelatedWork W115553845 @default.
- W4387583652 hasRelatedWork W1994961320 @default.
- W4387583652 hasRelatedWork W2088988140 @default.
- W4387583652 hasRelatedWork W2171912896 @default.
- W4387583652 hasRelatedWork W2378076731 @default.
- W4387583652 hasRelatedWork W2971083503 @default.
- W4387583652 hasRelatedWork W3210795196 @default.
- W4387583652 hasRelatedWork W4236696095 @default.
- W4387583652 hasRelatedWork W4244907930 @default.
- W4387583652 hasRelatedWork W4286888643 @default.
- W4387583652 isParatext "false" @default.
- W4387583652 isRetracted "false" @default.
- W4387583652 workType "article" @default.