Computer Engineering and Applications ›› 2025, Vol. 61 ›› Issue (18): 218-230.DOI: 10.3778/j.issn.1002-8331.2412-0294

• Graphics and Image Processing • Previous Articles     Next Articles

Geometric Transformation Combined with Image Enhancement for Small Target Detection in Aerial Images

QI Xiangming, LI Xiaolong   

  1. School of Software, Liaoning Technical University, Huludao, Liaoning 125100, China
  • Online:2025-09-15 Published:2025-09-15

几何变化结合图像增强的航拍图像小目标检测算法

齐向明,李晓龙   

  1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125100

Abstract: Aerial image small target detection is complex, leading to a decline in detection metrics. An algorithm combining geometric transformations and image enhancement is proposed. Using YOLOv8n as the baseline, DCNv2-CA-GEO extracts spatial and channel features in parallel, dynamically adjusting the convolutional and pooling kernels to quickly adapt to geometric changes. SPD-OK-CSP adjusts channel dimensions, capturing fine-grained features and enhancing image quality. Dysample optimizes upsampling, while Dyhead improves detection head performance. The Inner-Wise-MPD-IoU strategy balances sample features and optimizes generalization. Evaluated by mAP@0.5, mAP@0.5:0.95, Precision, and Recall, experiments on VisDrone2021 show improvements of 6.1 percentage points in mAP@0.5, 4.4 percentage points in mAP@0.5:0.95, 6.0 percentage points in Precision, and 5.0 percentage points in Recall. On LEVIR-Ship, the improvements are 3.4 percentage points in mAP@0.5, 2.2 percentage points in mAP@0.5:0.95, 3.1 percentage points in Precision, and 5.3 percentage points in Recall. Generalization tests on VOC2007+2012 demonstrate enhancements of 2.0 percentage points in mAP@0.5, 4.3 percentage points in mAP@0.5:0.95, 1.5 percentage points in Precision, and 3.1 percentage points in Recall, indicating good robustness.

Key words: aerial image, small target detection, YOLOv8n, SPDConv, Omni-Kernel, DCNv2, Dyhead

摘要: 航拍图像小目标检测场景复杂,检测指标下降,提出几何变化结合图像增强的航拍图像小目标检测算法。以YOLOv8n为基线,DCNv2-CA-GEO并行提取空间特征和通道特征,动态调整卷积核和池化核,快速适应几何变化;SPD-OK-CSP调整通道维度,捕捉细粒度特征,跨阶段全局特征感知,多尺度特征学习,提高图像增强;Dysample动态调整差分采样点,优化上采样计算;Dyhead动态调整特征张量维度,整合上下文信息,优化检测头性能;Inner-Wise-MPD-IoU多视角增益分配策略,平衡不同质量样本特征,准确回归真实框,优化泛化能力。评价指标mAP@0.5、mAP@0.5:0.95、Precision、Recall,消融和对比实验在VisDrone2021上分别提升6.1、4.4、6.0、5.0个百分点,在LEVIR-Ship上分别提升3.4、2.2、3.1、5.3个百分点,优于基线算法和次优算法;泛化实验在VOC2007+2012上分别提升2.0、4.3、1.5、3.1个百分点,鲁棒性良好。

关键词: 航拍图像, 小目标检测, YOLOv8n, SPDConv, Omni-Kernel, DCNv2, Dyhead