  We propose an object detection method that improves the accuracy of the
conventional SSD (Single Shot Multibox Detector), which is one of the top
object detection algorithms in both aspects of accuracy and speed. The
performance of a deep network is known to be improved as the number of feature
maps increases. However, it is difficult to improve the performance by simply
raising the number of feature maps. In this paper, we propose and analyze how
to use feature maps effectively to improve the performance of the conventional
SSD. The enhanced performance was obtained by changing the structure close to
the classifier network, rather than growing layers close to the input data,
e.g., by replacing VGGNet with ResNet. The proposed network is suitable for
sharing the weights in the classifier networks, by which property, the training
can be faster with better generalization power. For the Pascal VOC 2007 test
set trained with VOC 2007 and VOC 2012 training sets, the proposed network with
the input size of 300 x 300 achieved 78.5% mAP (mean average precision) at the
speed of 35.0 FPS (frame per second), while the network with a 512 x 512 sized
input achieved 80.8% mAP at 16.6 FPS using Nvidia Titan X GPU. The proposed
network shows state-of-the-art mAP, which is better than those of the
conventional SSD, YOLO, Faster-RCNN and RFCN. Also, it is faster than
Faster-RCNN and RFCN.
