P2AW-YOLO11n: Improving Drone-Based Small Object Detection via AFPN and Enhanced Loss Functions

In recent years, the decreasing production costs and rapid technological advancements of Unmanned Aerial Vehicles (UAVs) have led to their widespread adoption in critical domains such as agricultural plant protection, power line inspection, emergency rescue, and homeland security. Despite their growing utility, UAVs face significant challenges when performing tasks that require real-time object detection. The images captured by their onboard vision systems often feature densely clustered, small-sized targets, which strain the capabilities of existing detection algorithms. For instance, the YOLO11n model struggles with substantial feature loss due to its downsampling operation, limiting its effectiveness in detecting small objects in complex scenarios. To address these limitations, this paper introduces P2AW-YOLO11n, an enhanced model derived from YOLO11n, specifically designed to improve small object detection. The proposed model incorporates three key innovations. First, Adaptive Feature Pyramid Network (AFPN) is integrated into the neck of the model to enable more flexible feature fusion through adaptive mechanisms such as dynamic weight learning and cross-scale interactions, effectively mitigating feature dilution. Second, the P2 detection head utilizes large-scale 160 × 160 feature maps to capture finer target details, significantly enhancing the model's ability to detect small objects. Third, a refined version of the Wise-IoU (WIoU) loss function is introduced to address challenges posed by low-quality samples, thereby improving the model's generalization capabilities. Extensive comparative and ablation experiments conducted on the VisDrone dataset demonstrate the efficacy of the proposed algorithm. P2AWYOLO11n achieves a 7.5% improvement in Precision, a 5.5% increase in Recall, and a 6.9% boost in mAP @ 50 compared to the baseline YOLO11n model. These results underscore the robustness and effectiveness of P2AW-YOLO11n in overcoming the challenges associated with small object detection, paving the way for enhanced UAV applications in complex environments.