北京大学学报（自然科学版）2026，Vol.62Issue(2)：237-252,16.DOI:10.13209/j.0479-8023.2025.063
YOLO11n-seg-RF:一种改进的轻量级岩石裂隙检测及分割算法
YOLO11n-seg-RF:An Improved Lightweight Rock Fracture Detection and Segmentation Algorithm
摘要
Abstract
To address the challenges of sample imbalance,insufficient learning of hard-to-classified samples,and difficulties in small target segmentation in rock fracture detection and segmentation tasks,this paper proposes YOLO11n-seg-RF,an improved lightweight algorithm based on YOLO11n-seg.The proposed method incorporates three key components:1)a Multi-Receptive Field Joint Enhanced Convolutional Block Attention Module(JECBAM)to enhance feature representation,2)a Grouped Channel Attention-based Feature Fusion Module(GCAConcat)for effective multi-scale feature integration,and 3)a Simplified Spatial Pyramid Pooling Fast module(SimSPPF)to optimize spatial information aggregation.Additionally,the Focaler-IoU loss function is adopted to improve segmentation accuracy for fine-grained and multi-branch fractures.Experimental results on a custom rock fracture dataset demonstrate superior performance.Detection metrics achieve 88.7%Precision(Box),77.5%Recall(Box),84.2%mAP0.5(Box),and 67.3%mAP0.5:0.95(Box).Segmentation metrics reach 78.5%Precision(Mask),68.0%Recall(Mask),68.0%mAP0.5(Mask),and 27.0%mAP0.5:0.95(Mask).The model achieves real-time inference at 144 FPS with only 2.47M parameters,outperforming baseline YOLO11n-seg and other mainstream instance segmentation models.Ablation studies confirm the effectiveness of each proposed module,showing significant improvements in detection/segmentation accuracy while reducing model complexity.Generalization experiments on public datasets(crack-seg and carparts-seg)demonstrate superior cross-domain performance,with mAP0.5(Box)and mAP0.5(Mask)exceeding comparative models.Practical validation in mining engineering applications reveals that the algorithm successfully identifies core fractures in borehole samples,enabling rapid estimation of uniaxial compressive strength through established porosity-compressive strength equations derived from fracture ratio analysis and uniaxial compression tests,thereby verifying the practical engineering value.关键词
岩石裂隙检测/实例分割/YOLO11n-seg/孔隙度/单轴抗压强度Key words
rock fracture detection/instance segmentation/YOLO11n-seg/porosity/uniaxial compressive strength引用本文复制引用
靳子越,李海涛,殷海晨,杨冠宇,陈宇龙,张海宽,李显涛,蔡少阳..YOLO11n-seg-RF:一种改进的轻量级岩石裂隙检测及分割算法[J].北京大学学报（自然科学版）,2026,62(2):237-252,16.基金项目
国家自然科学基金(52474174,52374206,52104090)资助 （52474174,52374206,52104090）
