  With the improvements in the object detection networks, several variations of
object detection networks have been achieved impressive performance. However,
the performance evaluation of most models has focused on detection accuracy,
and performance verification is mostly based on high-end GPU hardware. In this
paper, we propose real-time object detectors that guarantee balanced
performance for real-time systems on embedded platforms. The proposed model
utilizes the basic head structure of the RefineDet model, which is a variant of
the single-shot object detector (SSD). In order to ensure real-time
performance, CNN models with relatively shallow layers or fewer parameters have
been used as the backbone structure. In addition to the basic VGGNet and ResNet
structures, various backbone structures such as MobileNet, Xception, ResNeXt,
Inception-SENet, and SE-ResNeXt have been used for this purpose. Successful
training of object detection networks was achieved through an appropriate
combination of intermediate layers. The accuracy of the proposed detector was
estimated by the evaluation of the MS-COCO 2017 object detection dataset and
the inference speed on the NVIDIA Drive PX2 and Jetson Xavier boards were
tested to verify real-time performance in the embedded systems. The experiments
show that the proposed models ensure balanced performance in terms of accuracy
and inference speed in the embedded system environments. In addition, unlike
the high-end GPUs, the use of embedded GPUs involves several additional
concerns for efficient inference, which have been identified in this work. The
codes and models are publicly available on the web (link).
