Mobile CenterNet For Embedded Deep Learning Object Detection

Object detection is a fundamental task in computer vision with wide application prospect. And recent years, many novel methods are proposed to tackle this task. However, most algorithms suffer from high computation cost and long inference time, which makes them impossible to be deployed on embedded devices in real industrial application scenarios. In this paper, we propose the Mobile CenterNet to solve this problem. Our method is based on CenterNet but with some key improvements. To enhance detection performance, we adopt HRNet as a powerful backbone and introduce a category-balanced focal loss to deal with category imbalance problem. Moreover, to compress the model size as well as reduce inference time, knowledge distillation is employed to transfer knowledge from cumbersome model to a compact one. We conduct experiments on a large traffic detection dataset BDD100K and validate the effectiveness of all the modifications. Finally, our method achieves the 1st place in the Embedded Deep Learning Object Detection Model Compression Competition held in ICME 2020.