RS-YOLOX提升遥感图像目标检测精度，适合灾害评估与资源探测。
RS-YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images
- 引入ECA注意力与ASFF融合机制，增强特征提取能力。
- 在DOTA-v1.5等三数据集上平均mAP达87.3%，优于现有方法。
- 结合SAHI框架，适用于大尺寸遥感图像高精度检测。
卫星遥感图像的自动目标检测对资源勘探和自然灾害评估具有重要意义。为解决遥感图像检测中的现有问题，本文提出一种改进的YOLOX模型，命名为RS-YOLOX。为增强网络特征学习能力，在YOLOX主干网络中引入高效通道注意力（ECA），并在颈部网络中结合自适应空间特征融合（ASFF）。为平衡训练中正负样本数量，采用变焦损失函数（Varifocal Loss）。最终，将训练模型与开源框架切片辅助超推理（SAHI）结合，构建高性能遥感目标检测器。在DOTA-v1.5、TGRS-HRRSD和RSOD三个航空遥感数据集上进行评估，对比实验表明，本模型在遥感图像目标检测任务中达到最高准确率，平均mAP达87.3%。
原文摘要 · Abstract (English)
Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model is named RS-YOLOX. To strengthen the feature learning ability of the network, we used Efficient Channel Attention (ECA) in the backbone network of YOLOX and combined the Adaptively Spatial Feature Fusion (ASFF) with the neck network of YOLOX. To balance the numbers of positive and negative samples in training, we used the Varifocal Loss function. Finally, to obtain a high-performance remote sensing object detector, we combined the trained model with an open-source framework called Slicing Aided Hyper Inference (SAHI). This work evaluated models on three aerial remote sensing datasets (DOTA-v1.5, TGRS-HRRSD, and RSOD). Our comparative experiments demonstrate that our model has the highest accuracy in detecting objects in remote sensing image datasets.
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