DTA-Head: Dynamic Task Alignment Head for Regression and Classification in Small Object Detection

Detecting small targets poses significant challenges due to the limited feature information and the aggregation of features in deep feature maps. Existing single-stage detectors handle classification and regression separately, leading to inconsistent predictions and potential filtering by Non-Maximum Suppression (NMS). To address these issues, we propose the Dynamic Task Alignment (DTA) Head. This novel approach comprises two branches: regression and classification. The regression branch computes offsets and masks for feature alignment adopted by Deformable ConvNets v2 (DCNv2), while the classification branch enhances feature interaction through dynamic selection. The task-decomposition module separates features for each branch. Additionally, we introduce Diverse-Scale Channel-Specific Convolution (DSCSC) to apply diverse convolutions across specific channels and exchange channel information. Our methods achieved an [email protected] of 30.9% on the TinyPerson dataset, a 3.3% improvement over the original model’s 27.6% and outperforming other common models.