2021
DOI: 10.3390/s21062052
|View full text |Cite
|
Sign up to set email alerts
|

Deep Learning-Based Congestion Detection at Urban Intersections

Abstract: In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
5
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
8
1
1

Relationship

0
10

Authors

Journals

citations
Cited by 10 publications
(5 citation statements)
references
References 25 publications
0
5
0
Order By: Relevance
“…Currently, you only look once (YOLO) model [12] is being used to detect traffic that predicts based on the bounding boxes. In [13], the author uses the YOLOv3 model [14] in combination with the Lucas-Kanade method (LK) [15] to identify the vehicles in the region of interest (RoI) and calculate the speed of vehicles. Therefore, it is possible to determine the traffic status at urban intersections as illustrated in Figure 1.…”
Section: Related Workmentioning
confidence: 99%
“…Currently, you only look once (YOLO) model [12] is being used to detect traffic that predicts based on the bounding boxes. In [13], the author uses the YOLOv3 model [14] in combination with the Lucas-Kanade method (LK) [15] to identify the vehicles in the region of interest (RoI) and calculate the speed of vehicles. Therefore, it is possible to determine the traffic status at urban intersections as illustrated in Figure 1.…”
Section: Related Workmentioning
confidence: 99%
“…Nowadays, India become an urban country because every year the number of private vehicles always increases by about six million units per year, and 10-15 % comes from cars [6,7]. Japan, the USA, and Europe countries met the same problems.…”
Section: Introductionmentioning
confidence: 99%
“…Ohnishi et al [12] used digital images to measure slope displacement and achieve slope monitoring; however, this method requires expert knowledge. In recent years, deep learning technology has made breakthroughs, and deep Convolutional Neural Networks (CNNs) have gradually been applied to high-resolution remote sensing image classification [13][14][15][16], semantic segmentation [17][18][19], target detection [20][21][22][23], and so on. Therefore, the development of deep learning-based techniques for detecting slope failure and landslide is gradually emerging.…”
Section: Introductionmentioning
confidence: 99%