skip to main content
Idioma:
Primo Search
Clear Search Box
Search in: Busca Geral
Busca Avançada
Busca por Índices

Object detection using YOLO: challenges, architectural successors, datasets and applications

Diwan, Tausif ; Anirudh, G. ; Tembhurne, Jitendra V.

Multimedia tools and applications, 2023-03, Vol.82 (6), p.9243-9275 [Periódico revisado por pares]

New York: Springer US

Texto completo disponível

Citações Citado por
  • Título:
    Object detection using YOLO: challenges, architectural successors, datasets and applications
  • Autor: Diwan, Tausif ; Anirudh, G. ; Tembhurne, Jitendra V.
  • Assuntos: Accuracy ; Architecture ; Computer Communication Networks ; Computer Science ; Computer vision ; Data Structures and Information Theory ; Detectors ; Inference ; Machine learning ; Multimedia Information Systems ; Object recognition ; Performance enhancement ; Proposals ; Sensors ; Special Purpose and Application-Based Systems
  • É parte de: Multimedia tools and applications, 2023-03, Vol.82 (6), p.9243-9275
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
  • Descrição: Object detection is one of the predominant and challenging problems in computer vision. Over the decade, with the expeditious evolution of deep learning, researchers have extensively experimented and contributed in the performance enhancement of object detection and related tasks such as object classification, localization, and segmentation using underlying deep models. Broadly, object detectors are classified into two categories viz. two stage and single stage object detectors. Two stage detectors mainly focus on selective region proposals strategy via complex architecture; however, single stage detectors focus on all the spatial region proposals for the possible detection of objects via relatively simpler architecture in one shot. Performance of any object detector is evaluated through detection accuracy and inference time. Generally, the detection accuracy of two stage detectors outperforms single stage object detectors. However, the inference time of single stage detectors is better compared to its counterparts. Moreover, with the advent of YOLO (You Only Look Once) and its architectural successors, the detection accuracy is improving significantly and sometime it is better than two stage detectors. YOLOs are adopted in various applications majorly due to their faster inferences rather than considering detection accuracy. As an example, detection accuracies are 63.4 and 70 for YOLO and Fast-RCNN respectively, however, inference time is around 300 times faster in case of YOLO. In this paper, we present a comprehensive review of single stage object detectors specially YOLOs, regression formulation, their architecture advancements, and performance statistics. Moreover, we summarize the comparative illustration between two stage and single stage object detectors, among different versions of YOLOs, applications based on two stage detectors, and different versions of YOLOs along with the future research directions.
  • Editor: New York: Springer US
  • Idioma: Inglês
Links
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.