研究无人机空中图像检测算法用于采矿诱导的表面裂,基于改进的YOLOv10的基础上
Jiayong An1, Siyuan Dong2, Xuanli Wang1
1Xi'an University of Science and Technology, Xi'an, China.
Scientific reports
|August 17, 2025
概括
本研究介绍了YOLO-LSN,这是一种高效的AI模型,用于使用无人机图像检测采矿区的小型表面裂. 该模型提高了准确性,并减少了地质灾害警告和安全采矿操作的计算负载.
科学领域:
- 地理空间分析是什么?
- 人工智能的人工智能是人工智能.
- 地质工程是地质工程.
背景情况:
- 基于无人机的空中图像对于检测采矿引起的表面裂至关重要,有助于对地质灾害进行早期预警,并确保安全生产.
- 挑战包括小裂大小,复杂的形态,尺度变化和不均的分布,加之是有限的无人机计算能力.
研究的目的:
- 开发一个高效和轻量级的小目标检测模型 (YOLO-LSN) 用于在采矿地区基于无人机的裂检测.
- 优化裂特征提取,提高复杂环境中小裂的检测.
主要方法:
- 引入了一种轻量级的动态对齐检测头 (LDADH),用于多级特征融合和动态感应场调整.
- 开发了一个小物体特征增强金字塔 (SOFEP),以改善小裂细节的表示.
- 提出了一个加权损失函数,将标准化瓦瑟斯坦距离 (NWD) 和 IoU 结合起来,以提高定位精度.
主要成果:
- 在定制挖矿裂数据集上实现了12%的mAP@0.5改进和17%的参数减少.
- 在VisDrone2019数据集上表现出有效性,mAP@0.5为0.422 (+11.6%).
- 验证了该模型的效率和可靠性,用于基于无人机的小型物体检测.
结论:
- YOLO-LSN提供了一种有效且计算效率高的解决方案,用于使用无人机图像检测小表面裂.
- 该模型增强了地质危险预警系统,并为更安全的采矿活动做出了贡献.
- 拟议的方法解决了无人机用于环境监测的小型物体检测的关键挑战.
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