P2lite-yolo: Detection-scale Reallocation for Tiny Object Detection in Uav Imagery

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

In UAV imagery, object scales are heavily biased towards the tiny region, yet general-purpose object detectors typically employ fixed multi-scale prediction configurations. There is a significant mismatch between the spatial detail loss caused by continuous downsampling and the prediction scale setup. To address this issue, this paper proposes P2Lite-YOLO, a scale-prior-driven prediction resource reallocation method for tiny UAV objects. First, the prediction requirements of different resolution features are explicitly characterized based on the object scale distribution in the training data. While keeping the number of detection heads unchanged, the original P3/P4/P5 prediction scales of YOLO11s are reallocated to P2/P3/P4, allowing the limited prediction capacity to prioritize high-resolution regions where tiny objects dominate. Second, a P2 collaborative representation is constructed via top-down deep semantic propagation and shallow spatial detail fusion, enabling high-resolution features to retain both fine-grained localization information and contextual discriminative ability. Simultaneously, a detection head capacity alignment strategy is adopted to mitigate the impact of structural adjustments on pre-trained parameter transfer. Statistics from the VisDrone2019 training set show that 68.30% of the 343,205 valid objects have an equivalent size of less than 16 pixels, confirming the necessity of allocating high-resolution prediction resources. Under a unified experimental protocol, P2Lite-YOLO achieves 43.11% mAP50 and 26.38% mAP50-95 with 9.129 M parameters, outperforming YOLO11s by 2.88 and 2.47 percentage points, respectively, and surpassing comparative models such as YOLO11m, YOLOv8m, Faster R-CNN, and RetinaNet. Further experiments indicate that retaining the independent P5 prediction branch after introducing the P2 high-resolution prediction does not yield performance gains. These results demonstrate that, compared to simply expanding the detection scales, reconfiguring multi-scale representation and prediction resources under a fixed prediction budget based on data scale priors is an effective strategy for improving tiny object detection in UAV imagery.