Open Access

ARTICLE

EHDC-YOLO: Enhancing Object Detection for UAV Imagery via Multi-Scale Edge and Detail Capture

Zhiyong Deng¹, Yanchen Ye², Jiangling Guo^1,*

1 College of Intelligent Science and Engineering, Jinan University, 206 Qianshan Road, Xiangzhou District, Zhuhai, 519000, China
2 School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

* Corresponding Author: Jiangling Guo. Email:

Computers, Materials & Continua 2026, 86(1), 1-18. https://doi.org/10.32604/cmc.2025.069090

Received 14 June 2025; Accepted 05 September 2025; Issue published 10 November 2025

Abstract

With the rapid expansion of drone applications, accurate detection of objects in aerial imagery has become crucial for intelligent transportation, urban management, and emergency rescue missions. However, existing methods face numerous challenges in practical deployment, including scale variation handling, feature degradation, and complex backgrounds. To address these issues, we propose Edge-enhanced and Detail-Capturing You Only Look Once (EHDC-YOLO), a novel framework for object detection in Unmanned Aerial Vehicle (UAV) imagery. Based on the You Only Look Once version 11 nano (YOLOv11n) baseline, EHDC-YOLO systematically introduces several architectural enhancements: (1) a Multi-Scale Edge Enhancement (MSEE) module that leverages multi-scale pooling and edge information to enhance boundary feature extraction; (2) an Enhanced Feature Pyramid Network (EFPN) that integrates P2-level features with Cross Stage Partial (CSP) structures and OmniKernel convolutions for better fine-grained representation; and (3) Dynamic Head (DyHead) with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability. Comprehensive experiments on the Vision meets Drones for Detection (VisDrone-DET) 2019 dataset demonstrate that EHDC-YOLO achieves significant improvements, increasing mean Average Precision (mAP)@0.5 from 33.2% to 46.1% (an absolute improvement of 12.9 percentage points) and mAP@0.5:0.95 from 19.5% to 28.0% (an absolute improvement of 8.5 percentage points) compared with the YOLOv11n baseline, while maintaining a reasonable parameter count (2.81 M vs the baseline’s 2.58 M). Further ablation studies confirm the effectiveness of each proposed component, while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.

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Keywords

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