Open Access

ARTICLE

YOLO-S3DT: A Small Target Detection Model for UAV Images Based on YOLOv8

Pengcheng Gao^*, Zhenjiang Li

School of Cyber Security, Gansu University of Political Science and Law, Lanzhou, 730070, China

* Corresponding Author: Pengcheng Gao. Email:

(This article belongs to the Special Issue: Intelligent Soft Computing Techniques for Enhancing Wireless Networks with Unmanned Aerial Vehicles)

Computers, Materials & Continua 2025, 82(3), 4555-4572. https://doi.org/10.32604/cmc.2025.060873

Received 12 November 2024; Accepted 13 December 2024; Issue published 06 March 2025

Abstract

The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles (UAV) has emerged as a prominent research focus. Due to the considerable distance between UAVs and the photographed objects, coupled with complex shooting environments, existing models often struggle to achieve accurate real-time target detection. In this paper, a You Only Look Once v8 (YOLOv8) model is modified from four aspects: the detection head, the up-sampling module, the feature extraction module, and the parameter optimization of positive sample screening, and the YOLO-S3DT model is proposed to improve the performance of the model for detecting small targets in aerial images. Experimental results show that all detection indexes of the proposed model are significantly improved without increasing the number of model parameters and with the limited growth of computation. Moreover, this model also has the best performance compared to other detecting models, demonstrating its advancement within this category of tasks.

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