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. 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. 

针对岩石裂隙检测及分割任务中样本分布不均衡、难分类样本学习不足以及小目标分割困难等问题, 提出一种改进YOLO11n-seg 的轻量级岩石裂隙检测及分割算法YOLO11n-seg-RF。该算法设计多感受野联合增强注意力模块JECBAM、基于分组像素级注意力的特征融合模块GCAConcat以及高效的快速空间金字塔池化模块SimSPPF, 并采用Focaler-IoU损失函数, 提高对细小且多分支裂隙的分割精度和效率。实验结果表明, YOLO11n-seg-RF在自制岩石裂隙数据集上表现出色。检测精度指标方面, Precision (Box)达到88.7%, Recall (Box)达到77.5%, mAP0.5 (Box)达到84.2%, mAP0.5:0.95 (Box)达到67.3%; 分割精度指标方面, Precision (Mask)达到78.5%, Recall (Mask)达到68.6%, mAP0.5 (Mask)达到68.0%, mAP0.5:0.95 (Mask)达到27.0%。此外, 该算法的推理速度为144 FPS, 模型参数量为2.47 M, 均优于基线模型YOLO11n-seg及其他主流实例分割模型。通过消融实验, 验证了各改进模块的有效性, 可以显著地提升模型的检测和分割精度, 同时降低模型参数量并提高推理速度。泛化实验结果进一步表明, YOLO11n-seg-RF在公开数据集crack-seg和carparts-seg上均表现出优越的泛化能力, mAP0.5 (Box)和mAP0.5 (Mask)等关键指标均优于其他对比模型。在工程实例中, 将YOLO11n-seg-RF应用于矿井钻孔岩心裂隙识别, 通过计算裂隙占比并结合单轴压缩试验, 建立孔隙度–抗压强度方程, 能够快速推算单轴抗压强度, 验证了该算法在实际工程中的应用价值。