北京林业大学学报2025，Vol.47Issue(5)：14-25,12.DOI:10.12171/j.1000-1522.20240354
基于无人机影像和MDIEA-YOLO苗木识别模型的造林验收智能系统
An intelligent system for afforestation acceptance based on UAV images and MDIEA-YOLO seedling recognition model
摘要
Abstract
[Objective]Traditional afforestation acceptance methods are inefficient and struggle to adapt to complex scenarios.Meanwhile,the difficulty in directly using drone imagery for AI model input has hindered the intelligentization of afforestation acceptance.This study proposes a MDIEA-YOLO detection model based on drone imagery for afforestation acceptance scenarios.The goal is to achieve efficient identification and accurate counting of afforestation seedlings,thereby improving the precision and efficiency of afforestation acceptance and providing technical support for modernization of forestry management.[Method]To achieve the above objectives,this study developed a"multi-dimensional interaction enhanced attention module"(MDIEA).By integrating convolutional block attention mechanism and Shuffle Attention mechanism,MDIEA can efficiently process features of complex scenarios and small targets,significantly enhancing the network's parsing ability.Embedding MDIEA into the YOLOv8 feature extraction network,the refined channel and spatial attention weighting strengthened recognition of key features.In addition,the XIoU loss function was introduced to optimize model's boundary localization of small and overlapping targets,further improving detection accuracy.Ultimately,an end-to-end image preprocessing pipeline based on drone imagery and the MDIEA-YOLO model was constructed to enable automatic identification and counting of afforestation seedlings.[Result]In experiments conducted at the Jiangle State Forest Farm in Fujian Province of eastern China,the MDIEA-YOLO model achieved mAP0.5 values of 97.5％,96.1％,and 96.8％on the 1-year-old,2-year-old,and 3-year-old datasets,respectively,significantly outperforming other comparative models.Under different lighting and resolution conditions,the MDIEA-YOLO model's mAP0.5 values remained above 92％,demonstrating good robustness.When processing 100 images,the CPU and GPU processing efficiencies of MDIEA-YOLO model were similar,indicating high flexibility and adaptability of the system in practical applications.Comparison with manual inspection revealed that the system exhibited comparable or even higher accuracy and efficiency in key indicators,proving its reliability and practicality.[Conclusion]The afforestation acceptance drone image preprocessing system proposed in this study has effectively advanced the intelligentization of afforestation acceptance,significantly enhancing acceptance efficiency and accuracy.It offers a new technical solution for the afforestation acceptance field with broad application prospects.In the future,model the performance will continue to be optimized,and the dataset scale will be expanded to accommodate a wider range of application scenarios,promoting the modernization of forestry management.关键词
数字化造林验收/无人机(UAV)/图像识别/小目标检测/YOLOv8/注意力机制/损失函数优化Key words
digital afforestation acceptance/unmanned aerial vehicles(UAV)/image recognition/small object detection/YOLOv8/attention mechanism/optimization of loss function分类
计算机与自动化引用本文复制引用
王武魁,廉瑞峰,吴明晶,张大兴,石燕妮,谷亚宇..基于无人机影像和MDIEA-YOLO苗木识别模型的造林验收智能系统[J].北京林业大学学报,2025,47(5):14-25,12.基金项目
城市科技与精细化管理项目(Z221100005222108),福建省将乐国有林场基于无人机影像的造林监管自动化系统研建(20220517). （Z221100005222108）
