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To address the problems that existing foreign object detection algorithms for underground mine conveyor belts mainly model cross-channel dependencies through channel dimensionality reduction, resulting in high computational overhead and limited detection accuracy, a foreign object detection method based on attention-scale fusion and cross-spatial learning, termed ES-YOLO, was proposed. Firstly, an Efficient Multi-Scale Attention (EMA) module was introduced into the backbone network. By reshaping channels into the batch dimension and grouping channel dimensions into multiple sub-features, spatial semantic features were evenly distributed across different feature groups. Subsequently, a Scale Sequence Feature Fusion (SSFF) module was introduced to improve the network's capability for multi-scale feature extraction. By horizontally stacking feature maps at different scales and applying 3D convolution to capture scale-sequence features, the module effectively integrated high-dimensional semantic information from deep feature maps with detailed information from shallow feature maps. Furthermore, a Dimension-Aware Selective Integration (DASI) module was introduced to adaptively assign weights to high-dimensional and low-dimensional feature maps, thereby improving the model's ability to detect small objects. Experimental results demonstrate that, compared with the baseline YOLOv8n model, the proposed method increases the mean average precision by 1.5 percentage points to 89.7%. The class-wise mean average precision for large ore blocks improve by 4.1 percentage points, while the model parameters are reduced by 18%. Compared with mainstream models, the proposed method has significant advantages in detection accuracy and lightweight, making it more suitable for real-time foreign object detection in underground conveyor belts in mines.

, authors=Bingwen Zhao¹, Wei Wei², Guangyao Yang³, Zhen Zheng⁴, Bowen Pan⁵, authorsList=Bingwen Zhao, Wei Wei, Guangyao Yang, Zhen Zheng, Bowen Pan, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321893311556735481, articleId=1321893310713680375, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于注意尺度融合跨空间学习的矿山井下传送带异物检测方法, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对现有矿山井下传送带异物检测算法多以通道降维方式建模跨通道依赖，引起计算开销大且检测精度受限的问题，提出了一种基于注意尺度融合跨空间学习的异物检测方法（ES-YOLO）。首先，在主干网络中引入高效多尺度注意（EMA）模块，通过将通道重塑为批量维度，并将通道维度分组到多个子特征中，使空间语义特征在各特征组内均匀分布。随后，采用尺度序列特征融合（SSFF）模块提升网络的多尺度信息提取能力，该模块将不同尺度特征图水平堆叠，并利用3D卷积提取尺度序列特征，从而更好地融合深度特征图的高维信息与浅层特征图的细节信息。此外，维度感知选择性整合（DASI）模块自适应分配高维、低维特征图权重，增强模型对小目标的识别能力。试验结果表明，相比于基准模型YOLOv8n，该方法的平均精度均值提升了1.5个百分点，达89.7%；对大块矿料的类平均精度提升了4.1个百分点，参数量降低了18%；与主流模型相比，该模型在检测精度与轻量化程度方面均具有显著优势，更适合于矿山井下传送带异物实时检测场景。

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赵炳文（1966—），男，陕西眉县人，高级工程师，主要从事煤矿生产智能化方面的研究。E-mail:Zhaobingwen1966@163.com

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赵炳文（1966—），男，陕西眉县人，高级工程师，主要从事煤矿生产智能化方面的研究。E-mail:Zhaobingwen1966@163.com

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