Abstract: Aiming at the problems of diverse target scales,complex backgrounds,small target aggregation,and limited computing resources of drone platforms target detection of aerial images,an improved YOLOv8 target detection algorithm YOLOv8-CEBI is proposed.Firstly,a lightweight Context Guided module is introduced into the backbone network to significantly reduce the number of model parameters and computation.At the same time,a multi-scale attention mechanism EMA is introduced to capture fine-grained spatial information and improve the detection ability for small targets and complex backgrounds.Secondly,the weighted bidirectional feature pyramid network BIFPN is introduced to transform the neck,and the multi-scale feature fusion ability is enhanced under the premise of maintaining the parameter cost.Finally,the Inner-CIOU loss function is used to generate the auxi-liary bounding box to calculate the loss more accurately and accelerate the bounding box regression process.Experiments on the VisDrone dataset show that compared with the original YOLOv8s algorithm,the proposed method parameter amount is reduced by 51.3 %,the computation amount is reduced by 28.5 %,and the mAP50 is increased by 1.6 %.The proposed model ensures the improvement of accuracy and achieves a balance between reducing computing resources and ensuring accuracy.

Key words: Drones, Aerial images, Attention mechanism, Loss function, Lightweighting