MSG-YOLO: A Multi-Scale Statistical-Guided Network for Drone Object Detection

Object detection in drone-captured images must handle large-scale variations, cluttered backgrounds, and limited onboard computing power. To address these issues, this paper presents a lightweight detector named MSG-YOLO. The improvements lie in three aspects: feature extraction, multi-scale perception, and detection head design. We introduce an Attention Refinement Bottleneck (ARB) that combines depthwise separable convolutions with learnable channel scaling factors to achieve effective feature recalibration at very low parameter cost. We design an Omni-Receptive Field Module (ORFM) that employs multi-branch dilated convolutions to capture multi-scale context and incorporates second-order channel attention for better feature discrimination. A P2 detection head (160 × 160) is further added to the feature pyramid to enhance small-object response. Experiments show that ARB and ORFM work synergistically: each alone brings limited gain, but together they improve mAP@0.5 by 0.4 percentage points while reducing parameters by 0.13 M. On VisDrone2019, MSG-YOLO achieves 36.7% mAP@0.5, outperforming the baseline YOLOv11n by 4.3 points while using only 2.69M parameters. On the HazyDet benchmark, the method outperforms the baseline by 3.1 points, confirming robustness under low visibility. Ablation studies and visual analyses further reveal how each component contributes.