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Open Access

ARTICLE

Visual Detection Algorithms for Counter-UAV in Low-Altitude Air Defense

Minghui Li1, Hongbo Li1,*, Jiaqi Zhu2, Xupeng Zhang1
1 College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi’an, 710021, China
2 School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710139, China
* Corresponding Author: Hongbo Li. Email: email
(This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072406

Received 26 August 2025; Accepted 10 October 2025; Published online 21 November 2025

Abstract

To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports, this paper proposes an enhanced MBS-YOLO (Multi-Branch Small Target Detection YOLO) model for anti-drone object detection, based on the YOLOv8 architecture. To overcome the limitations of existing methods in detecting small objects within complex backgrounds, we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set, aiming to reduce the model’s computational complexity. To improve multi-scale feature fusion, we construct a Multi-Branch Feature Pyramid Network (MB-FPN) that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects. Additionally, a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle (UAV) targets, thereby improving detection performance across different scales. Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks. On the Det-Fly dataset, it improves precision by 3%, recall by 5.6%, and mAP50 by 4.5% compared with the baseline, while reducing parameters by 21.2%. Cross-validation on the VisDrone dataset further validates its robustness, yielding additional gains of 3.2% in precision, 6.1% in recall, and 4.8% in mAP50 over the original YOLOv8. These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios.

Keywords

Small target detection; anti-drone; yolov8; shared convolution; feature fusion network

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