PSODNet: Pretrained Scene-Aware Object Detection for Optical Remote Sensing Imagery

With the widespread application of remote sensing images in military and civilian fields, remote sensing object detection (RSOD) has become an important research direction. However, limited generalization and complex background interference have long been persistent challenges that hinder the development of RSOD. To address these issues, we propose a Pre-trained Scene-aware Object Detection Network (PSODNet). Firstly, we design an Enhanced Object Network (EON), which leverages a multi-head pretraining strategy to jointly train data from diverse sources, thereby expanding the scale of dataset and improve the generalization ability. Secondly, we introduce scenario-object relationship module to learn a multi-scale relationship map between objects and scenes, which is used to constrain the solution space of object detection, thereby enhancing performance in complex scenarios. Lastly, by using Label Smoothing Loss, PSODNet leverages mutual information of label to prevent extreme distributions of classification probabilities and reduce the risk of overfitting. In the experiment part, PSODNet was pretrained on multiple datasets and then fine-tuned on three datasets for validation. Results on three public datasets demonstrate that PSODNet outperforms existing models in detection performance by up to 2.4%, achieving a maximum mAP of up to 95%. Through visual interpretation of the relationship map, we found that PSODNet is able to bridge the semantic relevance between objects and scenes, demonstrating its potential in object detection in complex scenarios. Code is available at: https://github.com/creature-compound/PSODNet.