  In recent years, GoogleNet has garnered substantial attention as one of the
base convolutional neural networks (CNNs) to extract visual features for object
detection. However, it experiences challenges of contaminated deep features
when concatenating elements with different properties. Also, since GoogleNet is
not an entirely lightweight CNN, it still has many execution overheads to apply
to a resource-starved application domain. Therefore, a new CNNs, FactorNet, has
been proposed to overcome these functional challenges. The FactorNet CNN is
composed of multiple independent sub CNNs to encode different aspects of the
deep visual features and has far fewer execution overheads in terms of weight
parameters and floating-point operations. Incorporating FactorNet into the
Faster-RCNN framework proved that FactorNet gives \ignore{a 5\%} better
accuracy at a minimum and produces additional speedup over GoolgleNet
throughout the KITTI object detection benchmark data set in a real-time object
detection system.
