High-Precision and Lightweight Object Detection Model for Drone Aerial Photography Images
The current mainstream lightweight object detection models exhibit low detection accuracy in unmanned aerial vehicle(UAV)photography scenes.This study introduces a high-precision and lightweight aerial photography image object detection model based on YOLOv8s,named LEFE-YOLOv8.First,an enhanced feature extraction convolution(EFEConv)incorporating an attention mechanism was developed.It is integrated with partial channel convolution(PConv)and 1×1 convolution to create a lightweight enhanced feature extraction module.This integration augments the model's feature extraction capabilities and reduces the number of parameters and computational complexity.Subsequently,a lightweight dynamic upsampling operator module was incorporated into the feature fusion network,effectively addressing the information loss problem during the upsampling process in high-level feature networks.Finally,a detection head with multi-scale modules was designed to enhance the network model's multi-scale detection capabilities.The final experimental results demonstrate that,compared with the benchmark model,the improved model achieves an average accuracy of 42.3%and 83.9%on the VisDrone2019 and HIT-UAV datasets,respectively,with less than 10×106 parameters.These results establish the model's suitability for aerial image object detection tasks.
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