Multi-scale feature fusion with knowledge distillation for object detection in aerial imagery

Object detection in aerial images faces problems such as small and dense objects as well as occlusion. Existing methods usually adopt a fusion structure similar to a feature pyramid network (FPN-like), which introduces a small number of parameters but has a low detection accuracy. Therefore, to effectively balance the parameters of the model and the detection accuracy, this paper proposes a multi-scale feature fusion with knowledge distillation for object detection in aerial imagery (MFF-KD), which comprises a multi-scale feature fusion (MFF) network and channel and spatial attention knowledge distillation (CSAKD). First, we design a scale-aware feature fusion module (SAFFNet) in the MFF network, which is capable of reducing the model complexity while preserving more features of small objects. Secondly, we design an efficient multi-scale self-attention module (EMSA) that is integrated into the deep feature extraction process of the MFF network to capture continuous features of occluded objects. Finally, we propose a CSAKD knowledge distillation method, which enhances both foreground and effective background information in the student model, thereby improving the detection accuracy of the MFF network while controlling the growth of model parameters. We conducted extensive experiments on three publicly available aerial image datasets to validate the effectiveness of our method. The experimental results show that our method achieves good detection results on all three aerial image datasets. In addition, the parameter count of our method is 10.5M, and the real-time detection speed is 111 frames per second (FPS).