Huaneng Qingyang Coal Power Co., Ltd.
School of Energy and Mining Engineering, China University of Mining & Technology-Beijing
Huating Coal Industry Group Co., Ltd.
Zhalainuo'er Coal Industry Co., Ltd.
Huaneng Coal Technology Research Co., Ltd.
Huaneng Yunnan Diandong Energy Co., Ltd.
Shaanxi Mining Branch, Huaneng Coal Industry Co., Ltd.

针对现有基于深度学习的综放工作面混矸率检测方法在井下低照度、高粉尘、煤矸堆叠等复杂条件下存在煤矸识别精度低、分割效果差、模型参数量和运算量大、未实现混矸率的实时检测等问题，提出了一种基于GSL−YOLO模型的混矸率检测方法。GSL−YOLO模型在YOLOv8−seg的基础上进行以下改进：在主干网络中引入全局注意力机制（GAM），通过减少信息弥散和放大全局交互表示提高模型特征提取能力；选用具有高效局部聚合网络的空间金字塔池化（SPPELAN）模块，提升模型处理不同尺寸目标时的检测性能；采用轻量级非对称多级压缩检测头（LADH），降低模型的训练难度，同时提高推理速度。提出了一种基于类别分割掩码的混矸率计算方法，该方法基于煤流图像处理结果中的分割掩码信息，计算其中矸石的像素面积与总像素面积的比值，作为瞬时混矸率。实验结果表明：① GSL−YOLO模型的mAP@0.5∶0.95达96.1%，比YOLOv8−seg模型提高了0.8%。② GSL−YOLO模型的参数量为2.9×106个，浮点运算次数为11.4×109，模型权重为6.0 MiB，比YOLOv8−seg模型分别降低了12.1%，5.8%，11.8%，实现了模型的轻量化。③ GSL−YOLO模型在测试集上的帧率为12帧/s，基本满足实时检测要求。④ 与YOLO系列模型相比，GSL−YOLO模型分割效果最好，检测精度最高，参数量和运算量较少，综合性能最佳。⑤ 基于截取的综放工作面后部刮板输送机上煤流视频中的3帧图像，计算了瞬时混矸率，结果表明，提出的混矸率计算方法基本实现了综放工作面混矸率的实时计算。

Aiming to address the issues with current gangue mixed ratio detection methods in fully mechanized caving face based on deep learning, such as low accuracy of coal gangue identification, poor segmentation performance, large model parameters and computation load, and the inability to achieve real-time detection of gangue mixed ratio under complex conditions such as low lighting, high dust, and coal and angue stacking, the paper proposed a gangue mixed ratio detection method based on the GSL-YOLO model. The GSL-YOLO model introduced the following improvements to the YOLOv8-seg model: the incorporation of a global attention mechanism (GAM) in the backbone network to enhance feature extraction by reducing information dispersion and amplifying global interaction representation; the use of a spatial pyramid pooling with efficient local aggregation network (SPPELAN) module to improve detection performance for targets of varying sizes; and the adoption of a lightweight asymmetric dual-head (LADH) to reduce training difficulty while increasing inference speed. Additionally, a gangue mixed ratio calculation method based on category segmentation masks was proposed, which calculated the ratio of the pixel area of gangue to the total pixel area in the segmentation mask of coal flow images, serving as the instantaneous gangue mixed ratio. Experimental results showed that: ① The GSL-YOLO model achieved an mAP@0.5∶0.95 of 96.1%, which was 0.8% higher than the YOLOv8-seg model. ② The GSL-YOLO model had 2.9×106 parameters, 11.4×109 floating-point operations, and a model weight of 6.0 MiB, representing reductions of 12.1%, 5.8%, and 11.8% respectively compared to the YOLOv8-seg model, achieving model lightweighting. ③ The GSL-YOLO model achieved a frame rate of 12 frames per second on the test set, essentially meeting the requirements for real-time detection. ④ Compared with the YOLO series models, the GSL-YOLO model had the best segmentation effect, the highest detection accuracy, fewer parameters and computation load, and the best overall performance. ⑤ Based on three frames of images captured from the coal flow on the rear scraper conveyor of the fully mechanized caving face, the instantaneous gangue mixed ratio was calculated, and the results showed that the proposed method successfully realized real-time calculation of the gangue mixed ratio in fully mechanized caving face.

国家自然科学基金面上项目（52274207）；天地科技开采设计事业部科技创新基金项目（KJ-2021-KCMS-02）。

王福奇，王志峰，金建成，等. 基于GSL−YOLO模型的综放工作面混矸率检测方法[J]. 工矿自动化，2024，50（9）：59-65, 137.

WANG Fuqi, WANG Zhifeng, JIN Jiancheng, et al. Detection method for gangue mixed ratio in fully mechanized caving faces based on the GSL-YOLO model[J]. Journal of Mine Automation，2024，50（9）：59-65, 137.