LPAE-YOLOv8: lightweight aerial small object detection via LSE-Head and adaptive attention

Abstract

When detecting small objects in drone aerial imagery, the traditional YOLOv8 detection algorithm suffers from low accuracy, strong background interference, and model redundancy. Meanwhile, the hardware constraints of drones impose strict requirements on model size and computational complexity. To address these issues, this paper proposes an improved lightweight detection model named LPAE-YOLOv8, with the following modifications to the YOLOv8 architecture. First, an innovative detection head, LSE-Head, is introduced to balance the model’s parameter count and bounding box prediction accuracy. Second, the large-object detection layer is removed, and a new small-object detection layer is added to optimize the network structure and retain richer feature information. Third, an improved EfficiCIoU loss function is adopted to enhance the model’s bounding box regression capability. Finally, an improved convolutional module, ACMConv, which integrates the CMUNeXtBlock and Adaptive Attention (AA) mechanism, is designed to strengthen small-object perception. Experiments conducted on the VisDrone2019 and TinyPerson datasets demonstrate that LPAE-YOLOv8 achieves a lightweight design while maintaining high detection accuracy. On the VisDrone2019 dataset, mAP@0.5 increases by 16.9% compared with the baseline model, mAP@0.5:0.95 improves to 21.3%, and the number of parameters is reduced to 1.97 M, representing a 34.5% decrease from the original model. On the TinyPerson dataset, mAP@0.5 is enhanced from 14.4% to 18.7%. Ablation experiments verify the individual effectiveness of each improved module, while comparative experiments with mainstream algorithms demonstrate that the proposed model achieves an excellent balance among accuracy, complexity, and inference efficiency, fully proving its effectiveness and practical value for small object detection in drone aerial imagery.