Accurate and reliable pose estimation of boom-type roadheaders is the key to the forming quality of the tunneling face in coal mines, which is of great importance to improve tunneling efficiency and ensure the safety of coal mine production. The multi-laser-beam target-based visual localization method is an effective way to realize accurate and reliable pose estimation of a roadheader body. However, the complex background interference in coal mines brings great challenges to the stable and accurate segmentation and extraction of laser beam features, which has become the main problem faced by the long-distance visual positioning method of underground equipment. In this paper, a semantic segmentation network for underground laser beams in coal mines, RCEAU-Net, is proposed based on U-Net. The network introduces residual connections in the convolution of the encoder and decoder parts, which effectively fuses the underlying feature information and improves the gradient circulation performance of the network. At the same time, by introducing cascade multi-scale convolution in the skipping connection section, which compensates for the lack of contextual semantic information in U-Net and improves the segmentation effect of the network model on tiny laser beams at long distance. Finally, the introduction of an efficient multi-scale attention module with cross-spatial learning in the encoder enhances the feature extraction capability of the network. Furthermore, the laser beam target dataset (LBTD) is constructed based on laser beam target images collected from several coal mines, and the proposed RCEAU-Net model is then tested and verified. The experimental results show that, compared with the original U-Net, RCEAU-Net can ensure the real-time performance of laser beam segmentation while increasing the Accuracy by 0.19%, Precision by 2.53%, Recall by 22.01%, and Intersection and Union Ratio by 8.48%, which can meet the requirements of multi-laser-beam feature segmentation and extraction under complex backgrounds in coal mines, so as to further ensure the accuracy and stability of long-distance visual positioning for boom-type roadheaders and ensure the safe production in the working face.

中文翻译：

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RCEAU-Net：用于煤矿复杂背景激光束目标图像分割的级联多尺度卷积和基于注意力机制的网络

臂架掘进机准确可靠的位姿估计是煤矿掘进工作面成型质量的关键，对于提高掘进效率、保障煤矿安全生产具有重要意义。基于多激光束目标的视觉定位方法是实现掘进机本体准确可靠位姿估计的有效途径。然而煤矿井下复杂的背景干扰给激光束特征的稳定、准确分割和提取带来了巨大挑战，这已成为井下设备远距离视觉定位方法面临的主要问题。本文在U-Net的基础上提出了一种煤矿井下激光束语义分割网络RCEAU-Net。该网络在编码器和解码器部分的卷积中引入了残差连接，有效融合了底层特征信息，提高了网络的梯度循环性能。同时，通过在跳接部分引入级联多尺度卷积，弥补了U-Net中上下文语义信息的缺失，提高了网络模型对远距离微小激光束的分割效果。最后，在编码器中引入具有跨空间学习的高效多尺度注意力模块，增强了网络的特征提取能力。此外，基于从多个煤矿采集的激光束目标图像构建了激光束目标数据集（LBTD），并对所提出的RCEAU-Net模型进行了测试和验证。实验结果表明，与原始U-Net相比，RCEAU-Net在保证激光束分割实时性的同时，Accuracy提高了0.19%，Precision提高了2.53%，Recall提高了22.01%，Intersection and Union提高了22.01%比提高了8.48%，可以满足煤矿复杂背景下多激光束特征分割和提取的要求，从而进一步保证悬臂式掘进机远距离视觉定位的准确性和稳定性，保证掘进机的工作效率。工作面安全生产。