  Vehicle color recognition plays an important role in intelligent traffic
management and criminal investigation assistance. However, the current vehicle
color recognition research involves at most 13 types of colors and the
recognition accuracy is low, which is difficult to meet practical applications.
To this end, this paper has built a benchmark dataset (Vehicle Color-24) that
includes 24 types of vehicle colors, including 10091 vehicle pictures taken
from 100 hours of urban road surveillance videos. In addition, in order to
solve the problem of long tail distribution in Vehicle Color-24 dataset and low
recognition rate of existing methods, this paper proposes a Smooth Modulated
Neural Network with Multi-layer Feature Representation (SMNN-MFR) is used for
24 types of vehicle color recognition. SMNN-MFR includes four parts: feature
extraction, multi-scale feature fusion, suggestion frame generation and smooth
modulation. The model is trained and verified on the Vehicle Color-24 benchmark
dataset. Comprehensive experiments show that the average recognition accuracy
of 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 average
accuracy rate of the model when recognizing 8 types of colors is 97.25%, and
the detection accuracy of algorithms in similar databases is improved. At the
same time, visualization and ablation experiments also proved the rationality
of our network settings and the effectiveness of each module. The code and
database are published at: https://github.com/mendy-2013.
