Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

Mingdi Hu (), Yi Wu, Jiulun Fan and Bingyi Jing ()
Additional contact information
Mingdi Hu: School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Yi Wu: School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Jiulun Fan: School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Bingyi Jing: Department of Statistics & Data Science, Southern University of Science and Technology, Shenzhen 518055, China

Mathematics, 2022, vol. 10, issue 19, 1-16

Abstract: Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, ( J A D A R ), is proposed, where three layers of U N e t are embedded into R e t i n a N e t - 50 to obtain joint semantic fusion information. More precisely, the U N e t subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net ( F P N ) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The R a i n V e h i c l e C o l o r - 24 dataset is used to train the J A D A R for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of R e t i n a N e t - 50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision ( m A P ) of vehicle color recognition reaches 72.07 % , which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms.

Keywords: vehicle color recognition; low–high level joint task; object detection; joint semantic learning; deep neural network; rainy image recovery (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/19/3512/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/19/3512/ (text/html)

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:gam:jmathe:v:10:y:2022:i:19:p:3512-:d:925799

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().