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摘要: 高分辨率遥感图像在拍摄过程中包含了丰富的数据信息,使得目标与背景之间的差异减小,导致在检测目标时精度达不到所需要求,降低了目标检测的性能。基于YOLO深度学习算法,结合端到端坐标注意力(coordinate attention, CA)和轻量级网络模块GhostConv设计了一种轻量级网络模型GC-YOLOv5。CA沿水平和垂直方向分别对每个通道进行编码,使得注意力机制模块能够同时捕获具有精确位置信息的远程空间交互,并帮助网络更准确地定位感兴趣的目标。使用GhostConv模块代替原有的普通卷积模块(convolutional-batchnormal-SiLu,CBS),降低特征通道融合过程中的参数数量,减小最优模型的大小。使用公开的NWPU-VHR-10数据集进行了实验并在RSOD数据集上验证了模型的稳健性。结果表明,在NWPU-VHR-10数据集上的检测精度达到了96.5%,召回率达到了96.4%,mAP达到了97.7%。在RSOD数据集上也取得较好的效果。Abstract: High-resolution remote sensing images contain rich data and information, which reduce the difference between the target and the background, resulting in substandard detection accuracy and reduced target detection performance. Based on the deep learning algorithm You Only Look Once (YOLO), this study designed a lightweight network model GC-YOLOv5 by combining end-to-end coordinate attention (CA) and the lightweight network module GhostConv. The CA was employed to encode channels along the horizontal and vertical directions, enabling the attention mechanism module to simultaneously capture remote spatial interactions with precise location information and helping the network locate targets of interest more accurately. The original ordinary convolutional module convolutional-batchnormal-SiLu (CBS) was replaced by the GhostConv module, reducing the number of parameters in the feature channel fusion process and the size of the optimal model. Experiments were conducted on the GC-YOLOv5 using the publicly available NWPU-VHR-10 dataset, with the robustness of the model verified on the RSOD dataset. The results show that GC-YOLOv5 yielded a detection accuracy of 96.5% on the NWPU-VHR-10 dataset, with a recall rate of 96.4% and mAP of 97.7%. Moreover, GC-YOLOv5 achieved satisfactory results on the RSOD dataset.
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Key words:
- deep learning /
- remote sensing image /
- target detection /
- YOLOv5
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