Research on UAV visible light small target detection method based on improved YOLOv8
10.19745/j.1003-8868.2025001
- VernacularTitle:基于改进YOLOv8的无人机可见光小目标检测方法研究
- Author:
Jun XIE
1
;
Qin-wen PING
1
;
Bin-yue CAO
1
;
Bing-wen LIU
1
;
Mi HE
1
Author Information
1. 陆军军医大学生物医学工程与影像医学系,重庆 400038
- Publication Type:Journal Article
- Keywords:
YOLOv8;
unmanned aerial vehicle;
visible light image;
small target detection;
deep learning
- From:
Chinese Medical Equipment Journal
2025;46(1):1-6
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose an improved Y OLOv8-based visible small target detection method to solve the problems of the UAV visible light system in accuracy and timeliness when applied to measuring small targets.Methods A YOLOv8 network consisting of Backbone,Neck and Head was used as the base framework to construct an AGC-YOLO model.Firstly,a convolutional block attention module(CBAM)was incorporated into Backbone to improve the feature expression of the model;secondly,some traditional convolution modules were replaced with the changeable kernel convolution module AKconv to reduce the network parameters;finally,a Gold-YOLO module was involved in Neck to enhance the detection ability for targets with different sizes.VisDrone2019 dataset was used to carry out ablation and comparison experiments,and the efficacy of the AGC-YOLO model for detecting small targets was evaluated in terms of mean average precision(mAP),frames per second(FPS),giga floating-point operations per second(GFLOPs)and parameters.Results The AGC-YOLO model had the FPS,GFLOPs and parameters being 31.90,9.20 and 11.30 M respectively,meeting the real-time detection speed requirements of drones(FPS not lower than 30),in which the mAP50(the mAP with the intersection over union being 0.5)was increased by 15％,6％and 5％when compared with those of the lightweight YOLOv8n,Ghost-YOLO and YOLO-BiFPN models.Conclusion The method proposed behaves well in speed,decreased parameters and precision,and is worthy promoting for UAV visible small target detection.[Chinese Medical Equipment Journal,2025,46(1):1-6]
