Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks.
Sensors (Basel)
; 21(14)2021 Jul 12.
Article
en En
| MEDLINE
| ID: mdl-34300491
ABSTRACT
In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to detect small objects in real time. For depth estimation, we introduce binocular vision to the monocular-based disparity estimation network, and the epipolar constraint is used to improve prediction accuracy. Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation. The results demonstrate that the proposed approach achieves better results compared to conventional methods.
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Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Sensors (Basel)
Año:
2021
Tipo del documento:
Article
País de afiliación:
Taiwán