Anchor-Free Object Detection Method in Remote Sensing Image via Adaptive Multi-Scale Feature Fusion

The characteristics of many types, dense distribution and the difference in scales of objects in remote sensing images will result in small objects difficult to be detected. Therefore, a remote sensing image anchor-free object detection method based on an adaptive multi-scale feature fusion (AMFF) and attention feature enhancement (AFE) mechanism is proposed in this paper. Firstly, the image features extracted by the backbone network are input into AMFF, which adopts an adaptive multi-scale feature fusion module to enhance feature reuse, so it can enrich feature information and enhance the multi-scale feature expression ability of the network. Secondly, the output of the features from AMFF is input into the detection head with AFE. AFE combines multi-branch dilated convolution and attention mechanism to enhance both the network’s multi-scale generalization ability of the object and the effective feature information. Finally, the detection results are obtained by classification and regression. Experiments with a variety of mainstream object detection algorithms on DIOR and NWPU VHR-10 public datasets show that the average detection accuracy of the proposed algorithm is 72.4% and 87.4%, which is 9.4 percentage points and 13.5 percentage points higher than that of the baseline network and 6.3 percentage points and 1.7 percentage points higher than that of the suboptimal results. The results demonstrate that the average detection accuracy is higher than that of the mainstream object detection algorithms. Meanwhile, the average detection accuracy of the baseline network is significantly improved, which can detect small-scale objects more accurately and effectively improve the detection accuracy of multi-scale objects.