Point-Based Weakly Semisupervised Oriented Vehicle Detection in Optical Remote Sensing Images

Vehicle detection is vital for urban planning and traffic management. Optical remote sensing imagery, known for its high resolution and extensive coverage, is ideal for this task. Traditional horizontal bounding box (HBB) annotations often include excessive background, leading to reduced accuracy, whereas oriented bounding box (OBB) annotations are more precise but costly and prone to human error. To address these issues, we propose the Oriented Group R-CNN + Student Model (OGR-SM) framework, a weakly semisupervised oriented vehicle detection method based on single-point annotations. It leverages a small amount of OBB annotations along with a large quantity of single-point annotations for training, achieving performance comparable to fully supervised learning with 100% complete annotations. Specifically, we train the teacher model (OGR) using a small set of accurately annotated OBBs and their corresponding single-point annotations. This model employs an instance-point driven proposal grouping strategy and a group-based proposal assignment strategy enhanced with point location to generate pseudo-OBBs from a large set of weakly annotated single-point data. We then utilize conventional oriented detectors as student models to perform the vehicle detection task in a standard manner. Extensive experiments on two datasets show that our framework, using limited accurate OBBs and many pseudo-OBBs, can achieve or surpass the accuracy of fully supervised models. Our method balances high-quality annotations with data availability, enhancing scalability and robustness for vehicle detection in remote sensing applications.