Position Guided Dynamic Receptive Field Network: A Small Object Detection Friendly to Optical and SAR Images

Object detection in remote sensing images (RSIs), including optical and SAR images, has emerged as a rapidly advancing field. However, the abundance of small objects in RSIs poses a significant challenge in designing a network structure with effective receptive fields to support accurate localization and classification. In this paper, we propose a position guided dynamic receptive field network (PG-DRFNet) for small object detection friendly to optical and SAR images. Specifically, PG-DRFNet overcomes the problem of small objects vanishing or being submerged in features by establishing a positional guidance relationship of small objects between different feature layers. Then, we design a combination head structure that utilizes additional supervised information extracted from small objects to make the model more effective and flexible. Moreover, a dynamic perception algorithm based on feature construction is developed to dynamically optimize the perception regions and feature hierarchies of the model, while seeking the optimal tradeoff between model accuracy and inference speed. Without bells and whistles, our model is robust to two modalities of remote sensing data, and our experiments are conducted on four benchmark RSI datasets, including DOTA-v2.0, VEDAI, SSDD, and HRSID. The experimental results achieve competitive performance with 59.01%, 84.06%, 90.06%, and 80.59% mAP, respectively. Code and models are released at https://github.com/BJUT-AIVBD/PG-DRFNet.