航空兵器2025，Vol.32Issue(2)：87-93,7.DOI:10.12132/ISSN.1673-5048.2024.0171
基于上下文表示的轻量化小目标检测算法研究
Research on Lightweight Small Object Detection Algorithm Based on Context Representation
李强 1崔江辉2
作者信息
- 1. 中国空空导弹研究院,河南洛阳 471009
- 2. 南京航空航天大学,南京 211000
- 折叠
摘要
Abstract
Compared with general object detection,small object detection is more challenging due to its low reso-lution and limited anti-interference noise ability.Fully utilizing contextual semantic information is of great significance for solving the problems of small object detection.This paper proposes a lightweight framework algorithm based on a contextual semantic fusion model,which is built upon the YOLOv7 model.This framework model consists of three parts:a backbone network,a multi-scale feature representation network,and a detection head.Among them,partial convolutional(PConv)is utilized to construct a backbone network(P-Net),which ensures detection performance while further reducing computational complexity.The convolutional self-attention model is employed into traditional fea-ture pyramid network(FPN)structures to reduce information loss during up-sampling and down-sampling processes,and a differential detection head is used to detect targets of different sizes.Comparative experiments on the aerial image tiny object detection(AI-TOD)dataset show that the proposed model achieves average precision of 21.6 and 161 frame/s on the AI-TOD benchmark respectively,surpassing other small detection models.In addition,the detection performance is superior to other traditional detection models under the condition of low parameter quantities and compu-tational complexity.The results of ablation experiments indicate that the improved models proposed in this paper are ef-fective.关键词
目标检测/上下文表示/轻量化网络/卷积自注意力/特征融合Key words
object detection/context representation/lightweight network/convolutional self-attention/feature fusion分类
武器工业引用本文复制引用
李强,崔江辉..基于上下文表示的轻量化小目标检测算法研究[J].航空兵器,2025,32(2):87-93,7.
