Drboxlight: A Light Object Detection Model for Remote Sensing Applications
Yizhao Gao, Lei Liu, Guowei Chen, Bin Lei · 2019
DrBox (detector using rotatable bounding box) achieves superior performance on detection of arbitrary rotated objects by applying rotatable bounding box that enables the network to learn rotational invariant property of objects. However, the current DrBox algorithm uses deep convolution architecture, which is redundant in model storage and inference. In this paper, we present a light weight detection model for remote sensing images. The proposed method, DrBoxLight, uses depthwise separable convolution to decrease model size. Remote sensing applications need strong generalization abilities of the model, which is usually a weak point of small models. We adopt knowledge distillation framework for training to strengthen the generalization ability of DrBoxLight. In the knowledge distillation process, a well-trained teacher network (DrBox) will provide supervision and guidance to student network (DrBoxLight) by optimizing the hint loss of feature extraction outputs and focal loss of soft labels from teacher network. The experiment results on our datasets demonstrate that DrBoxLight trained by knowledge distillation can achieve acceptable precision compared with a large model.