Real-Time Traffic Sign Detection Under Foggy Condition

Title

Real-Time Traffic Sign Detection Under Foggy Condition

Creator

Anthony R.; Biswas J.

Description

Traffic congestion becomes high in urban areas and using public and private transportation services. The image of traffic signs gets affected by fog, and the detection of traffic signs has become difficult. To solve this issue, the machine learning technique has been used. Convolution neural network helps to solve real-time problems; hence, it can be used in the study for detecting traffic signs under foggy condition. The study results revealed that the model network has accuracy of 99.8%, and the proposed algorithm detects a traffic sign under foggy conditions in 2s per frame. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Electrical Engineering, Vol-840, pp. 137-144.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Accuracy; Convolution neural network; Machine learning; Traffic sign

Coverage

Anthony R., Christ (Deemed to be University), Bangalore, India; Biswas J., Christ (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 18761100; ISBN: 978-981169011-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-16-9012-9_12

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128641071&doi=10.1007%2f978-981-16-9012-9_12&partnerID=40&md5=9f0cdec375e5fb6fb46acc8ee9e0a73f