基于改进的MoCo的遥感图像目标检测

摘要/Abstract

摘要： 卫星遥感图像的智能化处理存在着处理标注时标准不统一、数据分布不均匀的问题，导致有效样本不多、目标检测效果较差的现象。针对这种现象，提出一种基于MoCo无监督对比学习模型的目标检测算法，目标检测的框架采用以ResNet50为骨干网络的YOLOv5，使用对比学习得到的ResNet50的权重作为固定值不进行梯度迭代参与YOLOv5下游的检测任务训练。对比学习实验在AID数据集上进行，改进的MoCo v2的top-1精度最高达到95.888%。在下游的检测任务中，使用的是TGRS-HRRSD数据集，改进MoCo v2的预训练权重的mAP@.5:.95精度达到67.8%，较不使用预训练权重提高了5.6个百分点。结果证明改进的MoCo对比学习模型的有效性，在对比学习之后的下游检测任务中，检测精度也有所提高。

关键词: 无监督对比学习, 遥感图像检测, 注意力机制, YOLOv5

Abstract: In the intelligent processing of satellite remote sensing images, there are some problems such as inconsistent standards and uneven data distribution, resulting in few effective samples and poor object detection effect. Aiming at this phenomenon, an object detection algorithm based on MoCo unsupervised contrast learning model is proposed. The framework of object detection adopts YOLOv5 with ResNet50 as the backbone network, and the weight of ResNet50 obtained by contrastive learning is used as a fixed value to participate in the detection task training of YOLOv5 downstream without gradient iteration. The contrastive learning experiment is carried out on AID Dataset, and the top-1 accuracy of the improved MoCo v2 is 95.888%. In the downstream detection task, using the TGRS-HRRSD Dataset, the accuracy of mAP@.5:.95 with the improved MoCo v2 pre-training weight is 67.8%, which is 5.6 percentage points higher than that without the pre-training weight. The results show that the improved MoCo comparative learning model is effective, and the detection accuracy is also improved in the downstream detection tasks after the comparative learning.

Key words: unsupervised contrastive learning, remote sensing image detection, attention, YOLOv5

焦新泉, 李睿康, 陈建军. 基于改进的MoCo的遥感图像目标检测[J]. 计算机与现代化, 2022, 0(12): 88-94.

JIAO Xin-quan, LI Rui-kang, CHEN Jian-jun. Remote Sensing Image Object Detection Based on Improved MoCo[J]. Computer and Modernization, 2022, 0(12): 88-94.

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