Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

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, ((Formula presented.)), is proposed, where three layers of (Formula presented.) are embedded into (Formula presented.) to obtain joint semantic fusion information. More precisely, the (Formula presented.) 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 ((Formula presented.)) 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 (Formula presented.) dataset is used to train the (Formula presented.) 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 (Formula presented.) while improving its robustness. Testing on self-built and public real datasets, the mean average precision ((Formula presented.)) of vehicle color recognition reaches (Formula presented.), which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms.