EPFA-Net: An Enhanced Partial Feature Aggregation Network for Remote Sensing Object Detection

Deep learning models, including convolution-based and self-attention-based models, have been highly successful applied to remote sensing object detection owing to their powerful performance. However, convolution-based models are constrained by the locality of convolution, thereby limiting their detection performance; self-attention-based methods require a large number of training samples to achieve good performance and have low computational efficiency, which hinders their application in real-time detection tasks. To address these issues, we propose an enhanced partial feature aggregation network (EPFA-Net) for remote sensing object detection. In EPFA-Net, we design an enhanced partial feature aggregation (EPFA) module that consists of two parts. The first part is an efficient layer aggregation block that is used to extract local features. The second part adopts a partial feature aggregation (PFA) structure, enhancing gradient efficiency, and a shift-window attention block to extract global features, establishing long-range dependencies. Extensive experiments are conducted on two challenging remote sensing object detection datasets. Experimental results indicate that EPFA-Net is superior to four mainstream methods in detection performance. In addition, findings from ablation experiments suggest that EPFA-Net is efficient in inference speed because of the utilization of the PFA structure.