Jiachen Yu

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.

Martian crater detection plays a critical role in landing-site selection and route planning, and it directly influences whether a mission can be executed safely and whether scientific observations and surface material information can be reliably acquired and returned. These outcomes, in turn, affect subsequent investigations of Martian geology and environmental evolution. Despite the progress of recent detectors, missed detections still occur, especially for medium-to-large craters with weak or blurred rims and for small overlapping craters. At the same time, practical deployment often requires a lightweight architecture so that computational cost can be controlled under real operational constraints. To address these issues, we propose SKE-YOLO11, a lightweight model designed for robust crater detection. First, we construct KWConv and C3K2_KW to replace the standard convolution and the C3K2 module in YOLO11n. This design reduces computation while strengthening feature extraction for small craters. Second, we introduce a Strip Convolution Module (SCM) to enlarge the effective receptive field, which helps the network learn rim and texture cues of medium-to-large craters and reduces missed detections. Third, considering the geometric characteristics of crater annotations, we fuse EIoU and Inner-IoU into an Inner-EIoU loss to replace the CIoU used in YOLO11n, thus improving bounding-box regression. Experiments show that SKE-YOLO11 achieves 83.0% Precision, 75.0% Recall, and 82.8% mAP@0.5. Compared with YOLO11n, Recall and mAP@0.5 improve by 2.3% and 1.8%, and parameters and GFLOPs decrease by 0.08 M and 1.3, respectively.