提升无人机图像中微小目标检测精度，仅需改进YOLOv8即可
Enhanced Detection of Tiny Objects in Aerial Images
- 通过调整图像分辨率与数据增强提升小目标特征
- 融合SE和CBAM注意力模块的MoonNet使检测准确率显著提升
- 适合遥感图像、低空侦察等小目标检测场景
单阶段检测器如YOLOv8虽训练快速，但在检测小目标时表现欠佳，尤其在低分辨率、背景杂乱的航拍图像中更为明显。为此，本文提出四种易实现的增强策略：输入图像分辨率调整、数据增强、注意力机制及注意力模块的替代门控函数。实验表明，图像放大与合理使用增强可有效提升性能。进一步设计了基于多注意力模块的MoonNet架构，将Squeeze-and-Excitation（SE）块与卷积块注意力模块（CBAM）集成至YOLOv8主干网络，相较原始主干及单一注意力结构，检测精度显著提高。当与YOLC模型结合时，MoonNet在微小目标基准测试上达到当前最优性能。代码已开源：https://github.com/Kihyun11/MoonNet
原文摘要 · Abstract (English)
While one-stage detectors like YOLOv8 offer fast training speed, they often under-perform on detecting small objects as a trade-off. This becomes even more critical when detecting tiny objects in aerial imagery due to low-resolution targets and cluttered backgrounds. To address this, we introduce four enhancement strategies-input image resolution adjustment, data augmentation, attention mechanisms, and an alternative gating function for attention modules-that can be easily implemented on YOLOv8. We demonstrate that image size enlargement and the proper use of augmentation can lead to enhancement. Additionally, we designed a Mixture of Orthogonal Neural-modules Network (MoonNet) pipeline which consists of multiple attention-module-augmented CNNs. Two well-known attention modules, Squeeze-and-Excitation (SE) Block and Convolutional Block Attention Module (CBAM), were integrated into the backbone of YOLOv8 to form the MoonNet design, and the MoonNet backbone obtained improved detection accuracy compared to the original YOLOv8 backbone and single-type attention-module-augmented backbones. MoonNet further proved its adaptability and potential by achieving state-of-the-art performance on a tiny-object benchmark when integrated with the YOLC model. Our code is available at: https://github.com/Kihyun11/MoonNet
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。