图像识别智能放煤是实现智能化综放开采的重要技术之一，其核心在于通过监测煤流含矸率控制放煤口开闭。而井下低照度环境、煤岩块体的不规则形状及堆积叠压等因素是制约煤流含矸率预测的主要难题。针对煤流表面含矸率，构建了不同照度条件下的煤岩图像数据库，研究了照度及煤岩种类对图像分割性能的影响，开发了综放开采“放−运”全过程高仿真实验平台，提出了基于多照度融合与光流场优化的复杂场景下煤流表面投影面积含矸率高精度识别方法。其次，探讨了不同投影角度和投影方式对煤岩块体二维形态学特征提取的影响，明确了不同维度形态学特征之间的量化关系，构建了“尺寸+形状”特征融合的煤岩块体体积预测模型，用于合理预测煤流表面体积含矸率。最后，结合实验室和现场数据验证了所提方法的有效性。研究表明：随着照度的增大，2种煤岩组合形式的识别精度均先增大后稳定，并略有下降。通过合理设置照度(比如17 730 lx)，煤+泥岩复杂组合的mAP@0.5由88.7%(3 180 lx)提升至92.3%。通过引入多照度融合和光流场分析，可进一步提升复杂场景下的煤流表面投影面积含矸率识别精度和算法适应性。合理选择光源波长，可以放大煤岩块体相对漫反射率差异，增加煤岩块体图像特征区分度。合理的图像投影方式也有利于不规则形状煤岩块体投影面积预测，预测偏差由60%降低至10%以下。通过引入形状特征辅助体积预测模型，体积预测精度显著提高，决定系数由仅考虑尺寸特征模型的0.941 6提升至0.969 2。

Image-based intelligent control of longwall top coal caving (LTCC) is a critical technology for achieving intelligent coal mining. Its core lies in monitoring the rock mixed ratio (RMR) of coal flow to control the drawing opening. However, challenges such as low illumination in underground environments, the irregular shapes of coal-rock particles, and their accumulation and compression significantly hinder the prediction of RMR in coal flow. To address these issues, this study focuses on the surface RMR of coal flow. A coal-rock image database was constructed under various illumination conditions to investigate the influence of illumination and coal-rock types on image segmentation performance. A high-fidelity experimental platform for the “caving-transport” process in LTCC was developed, and a high-precision recognition method for surface projection area-based RMR of coal flow under complex scenarios was proposed, based on multi-illuminance fusion and optical flow optimization. Furthermore, the impact of different projection angles and methods on the extraction of two-dimensional morphological features of coal-rock particles was explored, and the quantitative relationships between morphological features across dimensions were clarified. A “size + shape” feature fusion model was developed to reasonably predict the surface volume-based RMR of coal flow. Finally, the proposed methods were validated using both laboratory and field data. The research findings indicate that as illumination increases, the recognition accuracy of the two coal-rock combination forms initially increases, stabilizes, and then slightly decreases. By setting a reasonable illumination level (e.g., 17 730 lx), the mAP@0.5 for the coal + mudstone combination improved from 88.7% (3 180 lx) to 92.3%. The introduction of multi-illuminance fusion and optical flow analysis further enhanced the recognition accuracy and adaptability of surface projection area-based RMR under complex scenarios. A reasonable selection of light source wavelength can amplify the relative diffuse reflectance differences of coal-rock particles, increasing the distinguishability of image features. Moreover, appropriate projection methods improve the accuracy of predicting the projection area of irregularly shaped coal-rock particles, reducing the prediction error from 60% to less than 10%. By incorporating shape features into the volume prediction model, the prediction accuracy was significantly improved, with the coefficient of determination increasing from 0.9416 (size-only model) to 0.9692.