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JACIII Vol.27 No.2 pp. 198-206
doi: 10.20965/jaciii.2023.p0198
(2023)

Research Paper:

3D Street Object Detection from Monocular Images Using Deep Learning and Depth Information

Wei Liu*,**,***, Tao Zhang*,**,***, Yun Ma*,**,***,†, and Longsheng Wei*,**,***

*School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

***Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

Corresponding author

Received:
April 20, 2022
Accepted:
November 2, 2022
Published:
March 20, 2023
Keywords:
3D detection, monocular image, deep learning, street object
Abstract

In this study, we present a three-dimensional (3D) object detection algorithm based on monocular images by constructing an end-to-end network, that incorporates depth information. The entire network consists of three parts. The first part includes the basic object detection neural network as the main body, that uses the region proposal network to obtain the two-dimensional (2D) region proposal of the object. The second part is the depth estimation branch network, that obtains the depth information of the object pixels and calculates the corresponding 3D point cloud. In the last part, concatenated features obtained from the aforementioned two parts are fed into the fully-connected layers. Subsequently, 2D and 3D detection results are obtained. Compared with certain existing methods, the accuracy of the detection results is improved in this study.

Cite this article as:
W. Liu, T. Zhang, Y. Ma, and L. Wei, “3D Street Object Detection from Monocular Images Using Deep Learning and Depth Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.2, pp. 198-206, 2023.
Data files:
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