MSHC-YOLO: multiscale heterogeneous convolution for small object detection in traffic scenes

Abstract

Small object detection in traffic monitoring suffers from a structural inefficiency in standard detectors: isotropic 3×3 convolutions treat all spatial directions uniformly, yet traffic objects exhibit strong anisotropic geometry—pedestrians are vertically elongated, vehicles are horizontally wide. We propose MSHC-YOLO, built on two complementary innovations. First, a Multi-Scale Heterogeneous Convolution (MSHC) module decomposes standard convolution into three parallel kernels with distinct shapes—square (3×3), horizontal strip (1×3), and vertical strip (3×1)—to capture direction-specific spatial structures at negligible parameter cost. Second, a P2-Guided Fine-Grained Detection Head extends the feature pyramid to stride 4, providing a 160×160 detection grid that resolves objects as small as 8×8 pixels. Critically, MSHC and the P2 head interact synergistically: heterogeneous kernels are most effective in shallow, highresolution layers where spatial detail is preserved. On VisDrone2019-DET, MSHC-YOLO achieves 37.5% mAP@0.5, outperforming YOLO11n by 4.2 points while adding only 0.21M parameters. Ablation reveals a super-linear gain— MSHC contributes +0.5 mAP in the standard P3 path but +2.0 mAP when combined with the P2 head, validating that the shallower the feature, the more kernel shape matters.