The vehicle color recognition based on enhanced Yolov5 neural network

Color features play a unique role in vehicle recognition. The color recognition algorithms based on deep learning neural networks are studied in this paper. A color recognition experiment is carried on to some typical deep learning neural networks, the result of the experiment proves that Yolov5 has faster training speed and higher accuracy for vehicle color recognition, so Yolov5 is chosen for the color recognition. The structure of yolov5 is optimized by adding C2f module replacing C3 module and adjusting the parameters of HSV color space when it is applied to identify 8 typical vehicle colors using BIT Vehicles data set. The modified Yolov5 make the accuracy of vehicle color recognition improved effectively to the complex color vehicles and part covered vehicles comparing with original yolov5 network.