A Refinement Method for Single-Stage Object Detection Based on Progressive Decoupled Task Alignment

The parallel branches with independent optimized classification and localization capabilities are widely used in single-stage object detection. Defects such as feature conflicts, low level of information interaction, and empirical sample allocation scheme lead to weak spatial consistency of the outputs from different branches. In this work, we propose a Progressive Decoupled Task Alignment (PDTA) that enhances the information interaction between tasks while reducing the degree of feature coupling, and adopts a strategy based on sample screening and learning to achieve task alignment. First, we design the Discrepant Feature Decoupling Module (DFDM) embedded with the novel Oriented Decoupling Convolution (ODC) for the coupled features of the shared input, and the features extracted by ODC are utilized for disentanglement through the feed-in scheme with differences. Second, the Probabilistic Mapping Interaction Head (PMI-Head) utilizes the probabilistic mapping method to enhance task-specific semantics by information interaction. Finally, the network's common attention to the content and position of the target is enhanced through the metric in the proposed Relevance-Guided Adaptive Task Alignment (RATA), in which an exponentially decaying manner is used to preserve the training samples that are more efficient for both tasks. During training, task-aligned learning is performed by Relevance-Guided Loss. Experiments on MS COCO and DIOR datasets demonstrate the effectiveness of our method, PDTA achieves better performance for object detection.