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Vehicle color recognition plays an important role in intelligent trafficmanagement and criminal investigation assistance. However, the current vehiclecolor recognition research involves at most 13 types of colors and therecognition accuracy is low, which is difficult to meet practical applications.To this end, this paper has built a benchmark dataset (Vehicle Color-24) thatincludes 24 types of vehicle colors, including 10091 vehicle pictures takenfrom 100 hours of urban road surveillance videos. In addition, in order tosolve the problem of long tail distribution in Vehicle Color-24 dataset and lowrecognition rate of existing methods, this paper proposes a Smooth ModulatedNeural Network with Multi-layer Feature Representation (SMNN-MFR) is used for24 types of vehicle color recognition. SMNN-MFR includes four parts: featureextraction, multi-scale feature fusion, suggestion frame generation and smoothmodulation. The model is trained and verified on the Vehicle Color-24 benchmarkdataset. Comprehensive experiments show that the average recognition accuracyof the algorithm in the 24 categories of color benchmark databases is 94.96%,which is 33.47% higher than the Faster RCNN network. In addition, the averageaccuracy rate of the model when recognizing 8 types of colors is 97.25%, andthe detection accuracy of algorithms in similar databases is improved. At thesame time, visualization and ablation experiments also proved the rationalityof our network settings and the effectiveness of each module. The code anddatabase are published at: https://github.com/mendy-2013.

Vehicle 24-Color Long Tail Recognition Based on Smooth Modulation Neural Network with Multi-layer Feature Representation