Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

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.