LS-YOLO:基于改进YOLOv8n的航拍小目标检测算法OA

In aerial images,the detection of small targets is challenged by factors such as small target size,indistinct features,and dense distribution,leading to significant issues with missed and false detections.To address these challenges,and in consideration of the limited computational resources of unmanned aerial vehicles,an improved small target detection algorithm based on YOLOv8n is proposed:LS-YOLO(Light and Scale-YOLO).First,to prevent small targets from being obscured by noise and redundant information during feature extraction and enhancement,the improved algorithm removes the final feature extraction layer in the backbone network and replaces the original P5 detection head with the P2 small target detection head.Second,a lightweight multi-scale feature extraction module,Lightweight Multi-scale Convolution(LMC),is introduced in the backbone network.This module enhances the extraction of multi-scale features for small targets while reducing computational cost and improving the efficiency of the algorithm.Finally,a new Shared Task Alignment Detection Head(STA-Head)is proposed to address feature misalignment between classification and regression tasks,enhancing detection accuracy and further reducing the model's parameters.Compared with the baseline YOLOv8n algorithm,the proposed LS-YOLO model achieves an 8%improvement in mean average precision(mAP)50 on the VisDrone2019 dataset,reaching 43.2%.The precision and recall increase by 8%and 6.7%,achieving 53.6%and 41.4%,respectively.The model's parameters are reduced by 56.6%,totaling only 1.3×106.LS-YOLO also performs well on the RSOD dataset,demonstrating strong generalization ability.