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Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

  • Autores: Deisy Chaves Sánchez, Surajit Saikia, Laura Fernández Robles Árbol académico, Enrique Alegre Gutiérrez Árbol académico, María Trujillo
  • Localización: Revista iberoamericana de automática e informática industrial ( RIAI ), ISSN-e 1697-7920, Vol. 15, Nº. 3, 2018, págs. 231-242
  • Idioma: español
  • DOI: 10.4995/riai.2018.10229
  • Títulos paralelos:
    • A Systematic Review on Object Localisation Methods in Images
  • Enlaces
  • Resumen
    • español

      Actualmente, muchas aplicaciones requieren localizar de forma precisa los objetos que aparecen en una imagen, para su posterior procesamiento. Este es el caso de la inspección visual en la industria, los sistemas de diagnóstico clínico asistido por computador, la detección de obstáculos en vehículos o en robots, entre otros. Sin embargo, diversos factores como la calidad de la imagen y la apariencia de los objetos a detectar, dificultan la localización automática. En este artículo realizamos una revisión sistemática de los principales métodos utilizados para localizar objetos, considerando desde los métodos basados en ventanas deslizantes, como el detector propuesto por Viola y Jones, hasta los métodos actuales que usan redes de aprendizaje profundo, tales como Faster-RCNNo Mask-RCNN. Para cada propuesta, describimos los detalles relevantes, considerando sus ventajas y desventajas, así como sus aplicaciones en diversas áreas. El artículo pretende proporcionar una revisión ordenada y condensada del estado del arte de estas técnicas, su utilidad y sus implementaciones a fin de facilitar su conocimiento y uso por cualquier investigador que requiera localizar objetos en imágenes digitales. Concluimos este trabajo resumiendo las ideas presentadas y discutiendo líneas de trabajo futuro.

    • English

      Currently, many applications require a precise localization of the objects that appear in an image, to later process them. This is the case of visual inspection in the industry, computer-aided clinical diagnostic systems, the obstacle detection in vehicles or in robots, among others. However, several factors such as the quality of the image and the appearance of the objects to be detected make this automatic location difficult. In this article, we carry out a systematic revision of the main methods used to locate objects by considering since the methods based on sliding windows, as the detector proposed by Viola and Jones, until the current methods that use deep learning networks, such as Faster-RCNN or Mask-RCNN. For each proposal, we describe the relevant details, considering their advantages and disadvantages, as well as the main applications of these methods in various areas. This paper aims to provide a clean and condensed review of the state of the art of these techniques, their usefulness and their implementations in order to facilitate their knowledge and use by any researcher that requires locating objects in digital images. We conclude this work by summarizing the main ideas presented and discussing the future trends of these methods.

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