A Rotating Object Detector with Convolutional Dynamic Adaptive Matching

Standard convolution sliding along a fixed direction in common convolutional neural networks (CNNs) is inconsistent with the direction of aerial targets, making it difficult to effectively extract features with high-aspect-ratio and arbitrary directional targets. To this end, We have fully considered the dynamic adaptability of remote sensing (RS) detectors in feature extraction and the balance of sample gradients during training and designed a plug-and-play dynamic rotation convolution with an adaptive alignment function. Specifically, we design dynamic convolutions in the backbone network that can be closely coupled with the spatial features of aerial targets. We design a network that can capture the rotation angle of aerial targets and dynamically adjust the spatial sampling position of the convolution to reduce the difference between the convolution and the target in directional space. In order to improve the stability of the network, a gradient adaptive equalization loss function is designed during training. The loss function we designed strengthens the gradient of high-quality samples, dynamically balancing the gradients of samples of different qualities to achieve stable training of the network. Sufficient experiments were conducted on the DOTA, HRSC-2016, and UCAS-AOD datasets to demonstrate the effectiveness of the proposed method and to achieve an effective balance between complexity and accuracy.