北京林业大学学报2025，Vol.47Issue(4)：142-154,13.DOI:10.12171/j.1000-1522.20240353
MAFF-YOLO:面向造林验收的明穴目标检测模型
MAFF-YOLO:a target detection model for planting holes in afforestation acceptance
摘要
Abstract
[Objective]To solve the problems like strong subjectivity,lack of scientific basis,and insufficient personnel in traditional afforestation acceptance,this paper proposes an afforestation hole detection model of MAFF-YOLO.It aims to automatically identify and count the number and location of afforestation holes,promoting the digital transformation of afforestation acceptance and improving efficiency and scientific accuracy.[Method]Based on the YOLOv8 model,MAFF-YOLO was obtained through multiple improvements.First,it used MobileNetV4 as the backbone network to increase parameters and layers,enhancing detection accuracy.Second,it added a normalization-based attention module(NAM)to better capture hole features and reduce false detections.Third,it replaced the feature fusion module with a cross-scale feature fusion module(CCFM),which integrated features of different scales and reduced computational load,improving detection of small holes.Fourth,it replaced the detection head with an RFAHead,which dynamically adjusted the receptive field based on data complexity and importance,thereby enhancing adaptability to different input features.Finally,the bounding box loss function was optimized to FocusCIoU to address sample imbalance and improve learning capability for key samples.[Result]MAFF-YOLO demonstrated high accuracy in identifying the number and location of planting holes.Compared with basic YOLOv8 model,its precision increased by 1 percentage point,mAP50 by 0.7 percentage points,and F0.5 by 0.6 percentage points.Moreover,the algorithm complexity was significantly reduced.[Conclusion]Under the same experimental conditions,MAFF-YOLO shows significant advantages over other existing methods in improving the detection performance of afforestation holes.It has been successfully integrated into an end-to-end detection system,providing effective technical support for the digitalization of afforestation acceptance and further enhancing the efficiency and scientific nature of the acceptance process.关键词
小目标检测/YOLOv8/算法/数字化造林验收/无人机/MobileNetV4/NAM/CCFMKey words
small object detection/YOLOv8/algorithms/digital afforestation acceptance/unmanned aerial vehicles(UAV)/MobileNetV4/NAM/CCFM分类
农业科技引用本文复制引用
石燕妮,王武魁,吴明晶,张大兴,廉瑞峰,谷亚宇..MAFF-YOLO:面向造林验收的明穴目标检测模型[J].北京林业大学学报,2025,47(4):142-154,13.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）
