煤矸识别技术是矿井智能化建设的关键技术之一，针对工作面低照度高粉尘环境造成的煤矸识别模型精度不高以及小目标煤矸难以识别的问题，提出一种基于CSPNet-YOLOv7目标检测算法的煤矸图像识别模型。采用跨阶段部分网络（Cross Stage Partial Network，CSPNet）改进YOLOv7模型的主干特征提取网络，优化梯度信息减少网络参数，同时采用递归特征金字塔（Recursive Feature Pyramid，RFP）和可切换卷积（Switchable Auto Convolution，SAC）替换颈部特征提取网络中简单的上下采样和普通卷积模块，并采用3次迁移训练进行不同宽度和深度的特征学习，增强网络的泛化能力。试验结果表明，CSPNet-YOLOv7模型的平均精度均值为97.53%，准确率为92.24%，召回率为97.91%，F₁得分为0.95，模型的参数量为30.85×10⁶，浮点运算次数为42.15×10⁹，每秒传输帧数为24.37 f/s，与YOLOv7模型相比，平均精度均值提高了7.46%，参数量和浮点运算次数分别降低了17.23%和60.41%，相较于FasterRCNN-Resnet50、YOLOv3、YOLOv4、MobileNet V2 -YOLOv4、YOLOv4-VGG、YOLOv5s模型、CSPNet-YOLOv7模型对煤矸识别的平均精度均值最高，同时参数量和浮点运算次数较小，在识别精度和速度之间有着较好的平衡。最后，通过井下现场试验验证了CSPNet-YOLOv7模型，为煤矸精准识别提供了有效技术手段。

The gangue recognition technology is one of the key technologies in the intelligent construction of mines. To address the problem of low accuracy of the gangue recognition model caused by low illumination and high dust environment at the working face and the difficulty of recognizing small target gangue, a coal gangue image recognition model based on CSPNet-YOLOv7 target detection algorithm is proposed. Cross Stage Partial Network (CSPNet) is used to improve the backbone feature extraction network of YOLOv7 model, optimize the gradient information to reduce the network parameters, while Recursive Feature Pyramid (RFP) and Switchable Auto Convolution (SAC) to replace the simple up and down sampling and normal convolution modules in the neck feature extraction network, and to enhance the generalization ability of the network by using three migration training for feature learning of different widths and depths. The experimental results show that the CSPNet-YOLOv7 model has an average accuracy mean of 97.53%, an accuracy rate of 92.24%, a recall rate of 97.91%, an F1 score of 0.95, a model parametric number of 30.85×10⁶, a floating point operation count of 42.15×10⁹, and a frame rate of 24.37 f/s transmitted per second, Compared to the YOLOv7 model, the average mean accuracy is improved by 7.46%, and the number of parameters and floating point operations are reduced by 17.23% and 60.41%, respectively, compared to the FasterRCNN-Resnet50, YOLOv3, YOLOv4, MobileNet V2 -YOLOv4, YOLOv4-VGG, YOLOv5s models. The CSPNet-YOLOv7 model has the highest average accuracy mean for coal gangue identification, while the number of parameters and floating point operations is small, which has a good balance between identification accuracy and speed. Finally, the CSPNet-YOLOv7 model is validated through downhole field tests, providing an effective technical means for accurate coal gangue identification.