Deep neural networks have proven increasingly important for automotive scene
understanding with new algorithms offering constant improvements of the
detection performance. However, there is little emphasis on experiences and
needs for deployment in embedded environments. We therefore perform a case
study of the deployment of two representative object detection networks on an
edge AI platform. In particular, we consider RetinaNet for image-based 2D
object detection and PointPillars for LiDAR-based 3D object detection. We
describe the modifications necessary to convert the algorithms from a PyTorch
training environment to the deployment environment taking into account the
available tools. We evaluate the runtime of the deployed DNN using two
different libraries, TensorRT and TorchScript. In our experiments, we observe
slight advantages of TensorRT for convolutional layers and TorchScript for
fully connected layers. We also study the trade-off between runtime and
performance, when selecting an optimized setup for deployment, and observe that
quantization significantly reduces the runtime while having only little impact
on the detection performance.

研究了两种不同的物体检测网络在嵌入式 AI 平台中的部署和运行环境，针对 TensorRT 和 TorchScript 两个库进行了 Runtime 和性能方面的对比分析，结果表明量化技术显著提高了部署效率而对检测性能的影响较小。