Traffic-Sign-Detection Algorithm Based on SK-EVC-YOLO

Traffic sign detection is an important research direction in the process of intelligent transportation in the Internet era, and plays a crucial role in ensuring traffic safety. The purpose of this research is to propose a traffic-sign-detection algorithm based on the selective kernel attention (SK a...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2023-09, Vol.11 (18), p.3873
Main Authors: Zhou, Faguo, Zu, Huichang, Li, Yang, Song, Yanan, Liao, Junbin, Zheng, Changshuo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
attention mechanism
Balances (scales)
Boxes
Computer networks
Deep learning
explicit visual center
Feature maps
Object recognition
Roads & highways
Signs
small-target detection
Target detection
Telecommunication systems
Traffic congestion
Traffic control
traffic sign
Traffic signs
Vehicles
YOLOv5
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traffic sign detection is an important research direction in the process of intelligent transportation in the Internet era, and plays a crucial role in ensuring traffic safety. The purpose of this research is to propose a traffic-sign-detection algorithm based on the selective kernel attention (SK attention), explicit visual center (EVC), and YOLOv5 model to address the problems of small targets, incomplete detection, and insufficient detection accuracy in natural and complex road situations. First, the feature map with a smaller receptive field in the backbone network is fused with other scale feature maps to increase the small target detection layer. Then, the SK attention mechanism is introduced to extract and weigh features at different scales and levels, enhancing the attention to the target. By fusing the explicit visual center to gather local area features within the layer, the detection effect of small targets is improved. According to the experiment results, the mean average precision (mAP) on the Tsinghua-Tencent Traffic Sign Dataset (TT100K) for the proposed algorithm is 88.5%, which is 4.6% higher than the original model, demonstrating the practicality of the detection of small traffic signs.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11183873