  In an autonomous driving system, it is essential to recognize vehicles,
pedestrians and cyclists from images. Besides the high accuracy of the
prediction, the requirement of real-time running brings new challenges for
convolutional network models. In this report, we introduce a real-time method
to detect the 2D objects from images. We aggregate several popular one-stage
object detectors and train the models of variety input strategies
independently, to yield better performance for accurate multi-scale detection
of each category, especially for small objects. For model acceleration, we
leverage TensorRT to optimize the inference time of our detection pipeline. As
shown in the leaderboard, our proposed detection framework ranks the 2nd place
with 75.00% L1 mAP and 69.72% L2 mAP in the real-time 2D detection track of the
Waymo Open Dataset Challenges, while our framework achieves the latency of
45.8ms/frame on an Nvidia Tesla V100 GPU.
