Multitarget Detection in Depth-Perception Traffic Scenarios

Qiao Peng, Dengyin Zhang and Nianyin Zeng

Mathematical Problems in Engineering, 2022, vol. 2022, 1-7

Abstract: Multitarget detection in complex traffic scenarios usually has many problems: missed detection of targets, difficult detection of small targets, etc. In order to solve these problems, this paper proposes a two-step detection model of depth-perception traffic scenarios to improve detection accuracy, mainly for three categories of frequently occurring targets: vehicles, person, and traffic signs. The first step is to use the optimized convolutional neural network (CNN) model to identify the existence of small targets, positioning them with candidate box. The second step is to obtain classification, location, and pixel-level segmentation of multitarget by using mask R-CNN based on the results of the first step. Without significantly reducing the detection speed, the two-step detection model can effectively improve the detection accuracy of complex traffic scenes containing multiple targets, especially small targets. In the actual testing dataset, compared with mask R-CNN, the mean average detection accuracy of multiple targets increased by 4.01% and the average precision of small targets has increased by 5.8%.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/5590514.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/5590514.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5590514

DOI: 10.1155/2022/5590514

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().