标题：SEMA-YOLO: Lightweight Small Object Detection in Remote Sensing Image via Shallow-Layer Enhancement and Multi-Scale Adaptation

期刊：Remote Sensing

第一印象：浅层增强（SLE）策略 "simple but work"

摘要：小物体检测在遥感领域仍然是一个挑战，因为在下采样过程中会丢失特征，并且复杂背景会造成干扰。本文提出了一种新颖的网络，称为SEMA-YOLO，作为基于YOLOv11的增强框架，结合了三项技术进步。通过从根本上减少信息损失并结合跨尺度特征融合机制，所提出的框架显著增强了小物体检测性能。首先，浅层增强（SLE）策略减少了主干网络的深度，并引入了小物体检测头，从而增加了特征图的大小，提高了小物体检测性能。然后，设计了全局上下文池增强自适应空间特征融合（GCP-ASFF）架构，以优化四个检测头之间的跨尺度特征交互。最后，引入了将感受野适应（RFA）与C3k2结构相结合的RFA-C3k2模块，以实现更精细的特征提取。SEMA-YOLO在复杂的城市环境和密集目标区域表现出显著优势，同时其泛化能力满足了不同场景下的检测需求。实验结果表明，SEMA-YOLO在RS-STOD数据集上实现了72.5%的mAP50分数，在AI-TOD数据集上实现了61.5%的mAP50分数，超越了最先进的模型。

作者：Zhenchuan Wu, Hang Zhen, Xiaoxinxi Zhang, Xuechen Bai, Xinghua Li

Original Title: Remote Sensing, Vol. 17, Pages 1917: SEMA-YOLO: Lightweight Small Object Detection in Remote Sensing Image via Shallow-Layer Enhancement and Multi-Scale Adaptation

Abstract: Small object detection remains a challenge in the remote sensing field due to feature loss during downsampling and interference from complex backgrounds. A novel network, termed SEMA-YOLO, is proposed in this paper as an enhanced YOLOv11-based framework incorporating three technical advancements. By fundamentally reducing information loss and incorporating a cross-scale feature fusion mechanism, the proposed framework significantly enhances small object detection performance. First, the Shallow Layer Enhancement (SLE) strategy reduces backbone depth and introduces small-object detection heads, thereby increasing feature map size and improving small object detection performance. Then, the Global Context Pooling-enhanced Adaptively Spatial Feature Fusion (GCP-ASFF) architecture is designed to optimize cross-scale feature interaction across four detection heads. Finally, the RFA-C3k2 module, which integrates Receptive Field Adaptation (RFA) with the C3k2 structure, is introduced to achieve more refined feature extraction. SEMA-YOLO demonstrates significant advantages in complex urban environments and dense target areas, while its generalization capability meets the detection requirements across diverse scenarios. The experimental results show that SEMA-YOLO achieves mAP50 scores of 72.5% on the RS-STOD dataset and 61.5% on the AI-TOD dataset, surpassing state-of-the-art models.

DOI: 10.3390/rs17111917

Link: https://www.mdpi.com/2072-4292/17/11/1917

#小物体检测# #深度学习# #特征融合# #遥感图像# #城市环境#