HPPE: HeatMap Positional Embedding for Background Noise Suppression in Object Detection

Despite the rapid proliferation of Transformer-based architectures in small object detection, mitigating background noise and improving query quality remain critical bottlenecks. To address these challenges, this study presents HeatMap Positional Embedding (HPPE), an adaptive optimization framework that intrinsically couples positional encoding with semantic detection priors through heatmap-guided masking. Furthermore, we develop a novel visualization scheme for HPPE, providing an intuitive perspective on feature embeddings to facilitate hyperparameter tuning. Building upon this mechanism, two specialized modules are proposed: the Multi-Scale ObjectBox-Heatmap Fusion Encoder (MOFE) and the HeatMap Induced High-Quality Queries for Decoder (HIQQ). These components are tailored to generate semantically rich queries while actively suppressing irrelevant background interference. By incorporating heatmap positional embeddings alongside standard baseline feature extractors like Linear-Snake Conv (LSConv), our approach effectively handles the massive diversity of small object categories and drastically minimizes the required number of decoder multi-head layers. Extensive evaluations demonstrate that our framework achieves absolute mAP improvements of 2.3\% on the small object benchmark (NWPU VHR-10) and 1.8\% on the general dataset (PASCAL VOC) compared to the baseline.