Exploring cross-branch information for semi-supervised remote sensing object detection

Semi-supervised object detection (SSOD) provides a promising solution to mitigate the annotation costs in remote sensing applications. Mainstream teacher-student based SSOD methods leverage unlabeled images through pseudo labeling, and their effectiveness is fundamentally limited by the inevitable noise in pseudo labels, particularly for remote sensing (RS) scenarios with complex backgrounds and dense, multi-scale and oriented objects. Current methods primarily focus on reducing pseudo label noise through category, scale and Intersection over Union information mining, as well as designing fine-grained confidence thresholding strategies. However, the inherent discrepancy between classification and localization reliability is neglected. In this study, with analyzing the characteristic discrepancies between the classification and localization branches, We propose artificial intelligence (AI) methodological innovation method named cross-branch information incorporation method (i.e., CBI-SSOD) to utilize these discrepancies to assist the training of the classification branch, and thus improve the performance of SSOD methods. Specifically, our method present two key AI innovations. Firstly, we propose a pretext task to extract cross-branch information, which can improve the classification ability by reinforce the consistent predictions between the classification branch and the pretext task. Besides, we propose a pseudo label reassignment approach to adjust the soft classification pseudo labels, and thus suppress pseudo label noise and improve the detection performance. Extensive experiments on Dataset for Object Detection in Aerial Images (DOTAv1.0) and DOTAv1.5 datasets validate the effectiveness and superiority of our method, and demonstrate the practical engineering impact of our method on RS applications and interpretation systems. • Localization converges faster and requires fewer samples compared to classification. • Distinct convergence patterns represent there are cross-branch information existed. • Cross-branch information can assist suppressing pseudo label noise in SSOD task. • We propose an SSOD framework for RS images to integrate cross-branch information. • Extensive experiments validate the effectiveness and superiority of our method.