MFZD-YOLOv10: multi-scale roadway object detection

Abstract This paper addresses the issues of insufficient detection accuracy for small targets, occlusion misjudgments, and network lightweighting in drone aerial image recognition, by proposing the MFZD-YOLOv10 algorithm for small target detection. To tackle difficulties caused by aerial photography including tiny target features, dense distribution, and severe occlusions, this paper introduces four primary innovations to improve the YOLOv10 network structure: (1) A novel multi-perception fusion SEC2f module is designed to replace the original C2f module, enhancing the ability of texture feature capturing for tiny targets; (2) Design a new module (GSPPF) of the reception field to enrich model in the process of feature extraction of multi-scale spatial information perception and integration ability, to enhance general characterization of the performance; (3)A novel ZModule feature fusion architecture is introduced to substitute traditional Concat operations, enabling the network to efficiently interact with cross-layer feature and integrate more feature information; (4)The ELA attention mechanism is incorporated to enhance the network’s sensitivity to key features and optimize the detection head structure. The high-resolution P2 detection head is used in place of the P4 and P5 detection heads to improve the localization accuracy of small targets. Compared to the original YOLOv10 model, experiments on the VisDrone dataset demonstrate that the improved network achieves 3.4% and 3.2% increases in detection precision and mAP50 respectively, while reducing parameters by 25%, effectively balancing detection performance and model efficiency. This provides a practical solution for UAV small target detection. The improved network’s generalization ability is further demonstrated on the DOTA dataset.