航空兵器2025，Vol.32Issue(3)：78-85,8.DOI:10.12132/ISSN.1673-5048.2024.0168
基于部分卷积与注意力融合检测头的小目标检测算法
Small Object Detection Algorithm Based on Partial Convolution and Attention Fusion Detection Head
摘要
Abstract
With the increasing utilization of unmanned aerial vehicles(UAVs),enhancing the detection perfor-mance of UAV aerial images has become increasingly crucial.This paper proposes a small object detection algorithm based on partial convolution and attention fusion detection head,aiming to address the limitations of current mainstream object detection algorithms in detecting small objects in aerial images.To improve spatial feature extraction and control network computing time,a more efficient FasterNet backbone network is introduced along with partial convolution(PConv)to reduce memory access and redundant calculations during deep convolution.The feature extraction network is optimized to enhance the detection effectiveness for small-sized targets.Additionally,a Dynamic Head is incorporat-ed into the detection head,effectively applying attention mechanism to improve overall detection performance.Finally,the bounding box loss function is optimized as Inner-ShapeIoU,focusing on shape and scale of the bounding box to im-prove the accuracy for bounding box regression calculation while utilizing auxiliary bounding boxes to expedite conver-gence speed.Experimental evaluations are conducted using the public dataset VisDrone2019.Compared with the origi-nal YOLOv8n algorithm,the proposed method achieves an 11.9％ increase in accuracy P and a 13.4％ increase in mAP50,indicating significant improvement in small object detection accuracy.关键词
小目标检测/深度学习/部分卷积/注意力机制/无人机Key words
small object detection/deep learning/partial convolution/attention mechanism/UAV分类
军事科技引用本文复制引用
彭升,朱凤华,周劲,朱高峰,王迎旭,陈月辉..基于部分卷积与注意力融合检测头的小目标检测算法[J].航空兵器,2025,32(3):78-85,8.基金项目
国家自然科学基金项目(62273164) （62273164）
