针对煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂工况环境因素导致煤矸识别存在误检、漏检以及检测精度低的问题，提出一种基于CFS-YOLO算法的煤矸智能识别模型。采用ConvNeXt V2（Convolutional Neural Network with NeXt Units Version 2）特征提取模块替换主干网络末端的2个C3（Cross Stage Partial Bottle Neck Mudule）模块，通过将掩码自动编码器（Masked Autoencoders, MAE）和全局响应归一化（Global Response Normalization, GRN）层添加到ConvNeXt架构中，有效缓解特征崩溃问题以及保持特征在网络传递过程中的多样性；采用Focal-EIOU（Focal and Efficient Intersection Over Union）损失函数替换原CIOU（Computer Intersection Over Union）损失函数，通过其Focal-Loss机制和调整样本权重的方式优化边界框回归任务中的样本不平衡问题，提高模型的收敛速度和定位精度；添加无参注意力机制（Simple Attention Mechanism, SimAM）于主干网络每个C3模块的后端，凭借其注意力权重自适应调整策略，提升模型对尺度变化较大或低分辨率煤矸目标关键特征的提取能力。通过消融试验和对比试验验证所提CFS-YOLO模型的有效性与优越性。试验结果表明：CFS-YOLO模型对于煤矸在煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂环境下的检测效果均得到有效提高，模型的平均精度均值达到90.2%，相较于原YOLOv5s模型的平均精度均值提高了3.7%，平均检测速度达到90.09 FPS，可充分满足煤矸实时检测的需求。同时与YOLOv5s、YOLOv7-tiny与YOLOv8n等6种YOLO系列算法相比，CFS-YOLO模型对煤矿复杂环境的适应性最强且综合检测性能最佳，可为煤矸的智能高效分选提供技术支持。

Aiming at the complex working conditions environmental factors such as high noise, low illumination, motion blur and mass gangue mixing in coal mines, which lead to the problems of misdetection, omission and low detection accuracy in gangue recognition, a gangue recognition model based on CFS-YOLO algorithm is proposed. The ConvNeXt V2（Convolutional Neural Network with NeXt Units Version 2）feature extraction module is adopted to replace the two C3（Cross stage partial bottle neck mudule）modules at the end of the backbone network, which effectively mitigates the feature collapse problem as well as maintains the diversity of the features in the network delivery process by adding Masked Autoencoder and Global Response Normalization layers to the ConvNeXt architecture. The Focal-EIOU (Focal and Efficient Intersection Over Union) loss function is adopted to replace the original CIOU (Computer Intersection Over Union) loss function to optimize the sample imbalance problem in the bounding box regression task by means of its Focal-Loss mechanism and adjusting the sample weights, which improves the convergence speed and localization accuracy of the model. The parameter-free attention mechanism （Simple Attention Mechanism, SimAM） is added to the back-end of each C3 module of the backbone network to enhance the model's ability of extracting key features of coal gangue targets with large scale variation or low resolution by virtue of its attention weight adaptive adjustment strategy. The effectiveness and superiority of the proposed CFS-YOLO model is verified by ablation and comparison experiments. The experimental results show that the CFS-YOLO model can effectively improve the detection effect of coal gangue under the complex environment of high noise, low illumination, motion blur and large amount of mixed coal gangue in coal mines. The mean Average Presicion (mAP) of the model reaches 90.2%, which is 3.7% higher than the mean Average Presicion (mAP) of the original YOLOv5s model, and the average detection speed reaches 90.09 FPS (Frames Per Second), which can fully satisfy the demand of real-time detection of coal gangue. Meanwhile, compared with six YOLO algorithms such as YOLOv5s, YOLOv7-tiny and YOLOv8n, the CFS-YOLO model has the strongest adaptability to the complex environment of coal mines and the best comprehensive detection performance, which can provide technical support for intelligent and efficient sorting of coal gangue.