Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

Hu, Mingdi1; Wu, Yi1; Fan, Jiulun1; Jing, Bingyi2

2022-10-01

10.3390/math10193512

MATHEMATICS   Impact Factor & Quartile

2227-7390

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, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet 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 (FPN) 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 Rain Vehicle Color-24 dataset is used to train the JADAR 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 RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms.

vehicle color recognition low-high level joint task object detection joint semantic learning deep neural network rainy image recovery

SCI

English

Corresponding

National Natural Science Foundation of China[62071378] ; Shaanxi Province International Science and Technology Cooperation Program[2022KW-04] ; Xi'an Science and Technology Plan Project[21XJZZ0072]

Mathematics

Mathematics

WOS:000867088100001

MDPI

Web of Science

1.Xian Univ Posts & Telecommun, Sch Commun & Informat Engn, Xian 710121, Peoples R China
2.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen 518055, Peoples R China

Hu, Mingdi,Wu, Yi,Fan, Jiulun,et al. Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions[J]. MATHEMATICS,2022,10(19).

Hu, Mingdi,Wu, Yi,Fan, Jiulun,&Jing, Bingyi.(2022).Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions.MATHEMATICS,10(19).

Hu, Mingdi,et al."Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions".MATHEMATICS 10.19(2022).