TY - GEN
T1 - Desempeño del Reconocimiento de Imágenes con Visión artificial
T2 - 21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023
AU - Lopez-Carreño, Joseph
AU - Calvo-Lavado, Cristhian
AU - Zarate-Perez, Eliseo
N1 - Publisher Copyright:
© 2023 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2023
Y1 - 2023
N2 - This study aimed to identify the existing techniques, applications, equipment, and technologies applied for recognizing images with artificial vision via a systematic review of the literature during the period 2020-2022. PRISMA was used for selecting and analyzing 142 articles obtained from the EBSCO, Engineering Source, ProQuest, and ScienceDirect databases. Studies that were not directly related to the proposed objectives were not included, leaving 28 articles for full-text review. The review results strongly suggest that Hopfield-type convolutional artificial neural networks are highly effective for image recognition and classification tasks. Similarly, the combination of technological tools such as YOLO, Roboflow, Python, and OpenCV shows that image processing and deep learning are driving new applications that improve the various performance metrics of these tasks. Therefore, artificial vision, unlike technologies that incorporate electronic devices with sensors, allows the interpretation of an environment with a high degree of representation of reality, confirming its robustness in the complexity of data processing.
AB - This study aimed to identify the existing techniques, applications, equipment, and technologies applied for recognizing images with artificial vision via a systematic review of the literature during the period 2020-2022. PRISMA was used for selecting and analyzing 142 articles obtained from the EBSCO, Engineering Source, ProQuest, and ScienceDirect databases. Studies that were not directly related to the proposed objectives were not included, leaving 28 articles for full-text review. The review results strongly suggest that Hopfield-type convolutional artificial neural networks are highly effective for image recognition and classification tasks. Similarly, the combination of technological tools such as YOLO, Roboflow, Python, and OpenCV shows that image processing and deep learning are driving new applications that improve the various performance metrics of these tasks. Therefore, artificial vision, unlike technologies that incorporate electronic devices with sensors, allows the interpretation of an environment with a high degree of representation of reality, confirming its robustness in the complexity of data processing.
KW - artificial intelligence
KW - computer vision
KW - image recognition
KW - neural networks
KW - object detection
UR - https://www.scopus.com/pages/publications/85172290829
M3 - Contribución a la conferencia
AN - SCOPUS:85172290829
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology
A2 - Larrondo Petrie, Maria M.
A2 - Texier, Jose
A2 - Matta, Rodolfo Andres Rivas
Y2 - 19 July 2023 through 21 July 2023
ER -