Autor(es):
Gonzalez, Dibet Garcia ; Castilla, Yusbel Chavez ; Shaharadaby, Somayeh ; Mackay, Ana Margarida ; Soares, Lúcia ; Guimarães, Pedro ; Morais, Francisco ; Puga, Joel Grisantes ; Meneses, Filipe ; Moreira, Adriano ; Machado, Miguel ; Adão, Telmo Miguel Oliveira ; Magalhães, Luís Gonzaga Mendes ; Guevara López, Miguel Angel
Data: 2021
Identificador Persistente: https://hdl.handle.net/1822/82409
Origem: RepositóriUM - Universidade do Minho
Assunto(s): Automatic Optical Inspection; Computer Vision; Deep Learning; Indoor Positioning; Microservices Architecture; Textile Industry
Descrição
Within a highly competitive market context, quality standards are vital for the textile industry, in which related procedures to assess respective manufacture still mainly rely on human-based visual inspection. Thereby, factors such as ergonomics, analytical subjectivity, tiredness and error susceptibility affect the employee's performance and comfort in particular and impact the economic healthiness of each company operating in this industry, generally. In this paper, a defect detection-oriented platform for quality control in the textile industry is proposed to tackle these issues and respective impacts, combining computer vision, deep learning, geolocation and communication technologies. The system under development can integrate and improve the production ecosystem of a textile company through a properly adapted information technology setup and associated functionalities such as automatic defect detection and classification, real-time monitoring of operators, among others.
