Mingyang Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mingyang Li has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Jiachen Yu
Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long N°S 100–460, Taipa, Macau, China
1 shared publication - Jiarui Liang
Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, N°S 100–460, Taipa, Macau, China
1 shared publication - Xiaolin Tian
Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
1 shared publication - Yikui Zhai
Wuyi University
1 shared publication
Latest Publications
- Open Access
- 15 June 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (12) , 6062
SPTD-YOLO: Small-Object-Aware Pyramidal and Task-Aligned Dynamic YOLO for UAV Small Object Detection
Unmanned aerial vehicle (UAV) object detection plays an essential role in modern visual perception, but it remains challenging because UAV imagery typically contains extremely small, densely distributed objects embedded in complex backgrounds. Conventional detectors, including the recent YOLOv12, are prone to losing critical spatial details during downsampling and often exhibit task misalignment between classification and localization, particularly under severe scale variations. To address these problems, this study proposes SPTD-YOLO, a small-object-aware pyramidal and task-aligned dynamic detector. Specifically, a Small Object Enhanced Pyramid (SOEP) is developed by incorporating SPDConv and CSPOmniKernel to preserve and refine shallow, fine-grained features. In addition, a high-resolution P2 detection layer is introduced to increase spatial grid density and strengthen the structural representation of tiny objects. Furthermore, a Task-Aligned Dynamic Detection Head (TADDH) is designed to decouple and coordinate classification and regression through dynamic convolution and a synergistic dual-gating mechanism. Experiments on VisDrone2019 show that SPTD-YOLO improves mAP@0.5 by 8.37% and mAP@0.5:0.95 by 5.11% over YOLOv12 while maintaining practical efficiency for UAV edge deployment.
- 15 June 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (12) , 6062
Open AccessSPTD-YOLO: Small-Object-Aware Pyramidal and Task-Aligned Dynamic YOLO for UAV Small Object Detection
Unmanned aerial vehicle (UAV) object detection plays an essential role in modern visual perception, but it remains challenging because UAV imagery typically contains extremely small, densely distributed objects embedded in complex backgrounds. Conventional detectors, including the recent YOLOv12, are prone to losing critical spatial details during downsampling and often exhibit task misalignment between classification and localization, particularly under severe scale variations. To address these problems, this study proposes SPTD-YOLO, a small-object-aware pyramidal and task-aligned dynamic detector. Specifically, a Small Object Enhanced Pyramid (SOEP) is developed by incorporating SPDConv and CSPOmniKernel to preserve and refine shallow, fine-grained features. In addition, a high-resolution P2 detection layer is introduced to increase spatial grid density and strengthen the structural representation of tiny objects. Furthermore, a Task-Aligned Dynamic Detection Head (TADDH) is designed to decouple and coordinate classification and regression through dynamic convolution and a synergistic dual-gating mechanism. Experiments on VisDrone2019 show that SPTD-YOLO improves mAP@0.5 by 8.37% and mAP@0.5:0.95 by 5.11% over YOLOv12 while maintaining practical efficiency for UAV edge deployment.