煤矿井下目标检测技术是建设智慧矿山不可或缺的内容，可以提供实时监测和识别能力，但井下光照不均匀、遮挡严重等因素造成了井下部分目标的信息缺失，极大降低了目标检测的准确率。基于此，提出一种改进YOLOv5s的井下残缺信息的多目标检测算法。考虑到残缺目标易与井下背景相混淆，该算法通过在YOLOv5s的Backbone部分融入 CBAM注意力模块，增强特征图中与残缺目标相关的通道和空间信息，从而增强抑制背景干扰能力。同时，为了有效地提取和强化小目标和被遮挡目标的细节特征，使用加权双向特征金字塔网络BiFPN代替原网络的PANet结构。其次，为了更好地适应井下残缺目标形状的变化，采用引入了额外边界框坐标信息的EIoU函数来优化原有损失函数。最后通过自建井下数据集对改进算法进行验证，实验结果表明：所提出的目标检测算法可以更好地解决井下监控环境中目标尺寸较小、部分区域被遮挡、纹理和形状变化对目标检测精度的影响，改进后模型的平均准确率达到了91.3%，相较于原模型提高了2.7%左右，F1-Score达到了90.0%，相较于原模型提高了1.9%左右。

Underground target detection technology in coal mines is an indispensable component of constructing a smart mine, providing real-time monitoring and recognition capabilities. However, factors such as uneven illumination and significant obstruction underground lead to incomplete information for certain targets, greatly reducing the accuracy of target detection. To address this, an improved algorithm for multi-objective real-time detection of incomplete information in coal mine underground is proposed, based on enhancing YOLOv5s. Recognizing that incomplete targets can easily be confused with the underground background, this algorithm incorporates a CBAM (Convolutional Block Attention Module) into the Backbone of YOLOv5s. This inclusion strengthens the channels and spatial information in the feature map relevant to incomplete targets, thus enhancing the suppression of background interference. Furthermore, to effectively extract and enhance detailed features of small and occluded targets, the Weighted Bi-directional Feature Pyramid Network (BiFPN) is employed in place of the original PANet structure. Additionally, to better adapt to the shape variations of incomplete underground targets, an Enhanced Intersection over Union (EIoU) function is introduced, incorporating additional bounding box coordinate information to optimize the existing loss function. Finally, the proposed algorithm is validated using a custom-built underground dataset. Experimental results demonstrate that the improved target detection algorithm effectively addresses challenges posed by small target sizes, partial occlusion, and variations in texture and shape within the underground monitoring environment. The enhanced model achieves an average accuracy of 91.3%, an improvement of approximately 2.7% over the original model, and an F1-Score of 90.0%, an improvement of around 1.9% over the original model.