基于改进YOLOv7的高分二号遥感影像滑坡识别算法研究
RESEARCH ON LANDSLIDE RECOGNITION ALGORITHM OF GF-2 REMOTE SENSING IMAGE BASED ON IMPROVED YOLOV7
黄园园 1丁雪 1杨钦淞2
作者信息
- 1. 云南师范大学信息学院 云南昆明 650500
- 2. 云南地质工程勘察设计研究院有限公司 云南昆明 650200
- 折叠
摘要
为快速准确地识别滑坡地质灾害,本文提出基于YOLOv7 的轻量滑坡检测模型.实验结果表明,改进后YOLOv7 网络模型整体mAP达到 94.6%,与原始YOLOv7 网络模型相比,参数量减少了 20.8M,计算量减少 47.3G,整体mAP0.5 提高 3.8%,检测速度FPS提高 14.3f/s,对滑坡灾害具有出色的检测效果.
Abstract
In order to quickly and accurately identify landslide geological hazards,we propose a lightweight landslide detection model based on YOLOv7.The experimental results show that the overall mAP of the improved YOLOv7 network model reaches 94.6%.Compared with the original YOLOv7 network model,the parameter and calculation amount have been reduced by 20.8M and 47.3G,respectively,while the overall mAP0.5 and the detection speed FPS have been increased by 3.8%and 14.3 f/s,respectively,indicating that the model has excellent detection performancefor landslide disasters.
关键词
滑坡/YOLOv7/结构重参数化/ASPP/Shuffle/Attention/云南怒江州Key words
Landslide/Yolov7/Structural Reparameterization/ASPP/Shuffle Attention/Nujiang,Yunnan引用本文复制引用
出版年
2024
NETL
NSTL
万方数据