Coal roadway roof rock fracture dataset

The identification of rock fractures in strata is crucial to enhance the intelligence of rock detection. Traditional fracture feature extraction methods suffer from issues such as low accuracy and low processing speed, necessitating the development of more effective approaches. To address this problem, this study proposes a new fracture instance segmentation network called FracSeg. Based on the SOLOv2 framework, we incorporated the Swin Transformer to optimize the backbone network and enhance fracture feature extraction. The CARAFE operator is utilized to replace nearest neighbor interpolation, reducing the computational overhead when merging multi-scale fracture features. Finally, the Shuffle Attention module was employed to improve the network's detection of fracture features. The experimental results demonstrate the superior performance of FracSeg, achieving a mask mAP of 78.2 on a custom dataset while maintaining an average inference speed of 28.2 fps. Even under complex conditions, it outperformed previous fracture segmentation networks in identifying crack structures in coal roadway roofs. Additionally, ablation studies verified the effectiveness of each optimized component in the FracSeg model.

岩层裂隙识别对于提升岩体检测的智能化水平至关重要。传统裂隙特征提取方法存在准确率偏低、处理速度缓慢等问题，亟需研发更为高效的解决方案。针对该问题，本研究提出一种新型裂隙实例分割网络FracSeg。该网络以SOLOv2框架为基础，引入Swin Transformer优化主干网络，以强化裂隙特征提取效果；采用CARAFE算子替代最近邻插值，降低多尺度裂隙特征融合过程中的计算开销；最终引入Shuffle Attention模块，提升网络对裂隙特征的检测能力。实验结果表明，FracSeg模型性能优异，在自定义数据集上实现了78.2的掩码平均精度均值（mask mAP），平均推理速度达28.2 fps。即便在复杂工况下，其在煤巷顶板裂隙结构识别任务中的表现也优于此前的各类裂隙分割网络。此外，消融实验验证了FracSeg模型中各优化组件的有效性。

搜集汇总

背景与挑战

背景概述

该数据集是一个煤巷顶板岩石裂缝的实例分割数据集，采用COCO格式，包含正弦和断裂带裂缝的图像和标注文件，旨在支持岩石裂缝检测的智能化研究。