OpenNet: Incremental Learning for Autonomous Driving Object Detection
  with Balanced Loss
Zezhou Wang Guitao Cao Xidong Xi Jiangtao Wang
Automated driving object detection has always been a challenging task in
computer vision due to environmental uncertainties. These uncertainties include
significant differences in object sizes and encountering the class unseen. It
may result in poor performance when traditional object detection models are
directly applied to automated driving detection. Because they usually presume
fixed categories of common traffic participants, such as pedestrians and cars.
Worsely, the huge class imbalance between common and novel classes further
exacerbates performance degradation. To address the issues stated, we propose
OpenNet to moderate the class imbalance with the Balanced Loss, which is based
on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient
reshaping to fast learn new classes with limited samples during incremental
learning. To against catastrophic forgetting, we employ normalized feature
distillation. By the way, we improve multi-scale detection robustness and
unknown class recognition through FPN and energy-based detection, respectively.
The Experimental results upon the CODA dataset show that the proposed method
can obtain better performance than that of the existing methods.