可转移的上下文网络用于从无人机远程传感图像中提取农村道路.
Jian Wang1,2, Renlong Wang1, Yahui Liu1
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
我们开发了TCNet,这是一个新的深度学习模型,用于从无人机图像中准确地提取农村道路. 在不需要再培训的情况下,TCNet显示出很好的可转移到新区域,克服了数据采集的挑战.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 从无人机图像中提取农村道路是复杂的,因为不同的道路特征和数据采集挑战.
- 高成本,手动注释需求和政策限制阻碍了农村无人机数据收集.
研究的目的:
- 提出一个可转移的上下文网络 (TCNet),以提高农村道路开采的准确性和可转移性.
- 解决农村道路开采的挑战,包括数据稀缺和复杂的道路环境.
主要方法:
- 利用稳定扩散进行数据增强,以生成多样化的训练样本.
- 集成的集群上下文变压器 (CCT),集群交叉注意力 (CCA) 和CBAM注意力用于模型可转移性.
- 开发了一个Dice-BCE-Lovasz (DBL) 损失函数,以提高对不平衡数据集的细分性能.
主要成果:
- 在只有23.67M参数的DeepGlobe和道路数据集上,TCNet取得了出色的表现.
- 向新的农村遥感数据集展示了出色的零射击可转移性.
- 在没有微调的情况下,在法国的勃第和中国的云南展示了有效的表现.
结论:
- 通过无人机成像,TCNet为农村道路开采提供了一种高效准确的解决方案.
- 拟议的模型克服了数据的局限性,并在不同地区展示了强大的泛化能力.
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