In the field of Small Object Detection (SOD), accurate classification and localization are crucial for detection performance. However, the inherent imbalance between classification and localization tasks can generate conflicting priorities, leading to suboptimal task coordination for small object detection. This imbalance is mainly caused by the different attention regions and the gradient competition between the two tasks during joint training. In this paper, we propose a Dual-Task Harmonization Framework (DTHF): First, we introduce a Feature Fusion-based Data Augmentation strategy (FF-DA), which amplifies boundary-aware patterns for localization while preserving critical semantic regions for classification, thereby aligning their region-of-interest priorities. Second, we design a Gradient Equilibrium Module (GEM) that dynamically balances tasks by altering the gradients, preventing one task from overwhelming the other during optimization. Experiments on the MS COCO and VisDrone datasets demonstrate that our method, compared to the baseline model, the experimental metrics of our method mAP in the VisDrone data set are improved by 2.0+%. Ablation studies validate that both FF-DA and GEM contribute synergistically, offering a unified solution to task imbalance in small object detection.